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Automating forest machines to optimize the forest value chain requires the ability to map the surroundings of the machine and to conduct accurate measurements of nearby trees. In the near-to-medium term, integrating a forest harvester with a mobile laser scanner system may have multiple applications, including real-time assistance of the harvester operator using laser-scanner-derived tree measurements and the collection of vast amounts of training data for large-scale airborne laser scanning-based surveys at the individual tree level. In this work, we present a comprehensive processing flow for a mobile laser scanning (MLS) system mounted on a forest harvester starting from the localization of the harvester under the forest canopy followed by accurate and automatic estimation of tree attributes, such as diameter at breast height (DBH) and stem curve. To evaluate our processing flow, we recorded and processed MLS data from a commercial thinning operation on three test strips with a total driven length ranging from 270 to 447 m in a managed Finnish spruce forest stand containing a total of 658 reference trees within a distance of 15 m from the harvester trajectory. Localization reference was obtained by a robotic total station, while reference tree attributes were derived using a high-quality handheld laser scanning system. As some applications of harvester-based MLS require real-time capabilities while others do not, we investigated the positioning accuracy both for real-time localization of the harvester and after the optimization of the full trajectory. In the real-time positioning mode, the absolute localization error was on average 2.44 m, while the corresponding error after the full optimization was 0.21 m. Applying our automatic stem diameter estimation algorithm for the constructed point clouds, we measured DBH and stem curve with a root-mean-square error (RMSE) of 3.2 cm and 3.6 cm, respectively, while detecting approximately 90% of the reference trees with DBH > 20 cm that were located within 15 m from the harvester trajectory. To achieve these results, we demonstrated a distance-adjusted bias correction method mitigating diameter estimation errors caused by the high beam divergence of the laser scanner used. [ABSTRACT FROM AUTHOR]

LiDAR has emerged as one of the most pivotal sensors in the field of navigation, owing to its expansive measurement range, high resolution, and adeptness in capturing intricate scene details. This significance is particularly pronounced in challenging navigation scenarios where GNSS signals encounter interference, such as within urban canyons and indoor environments. However, the copious volume of point cloud data poses a challenge, rendering traditional iterative closest point (ICP) methods inadequate in meeting real-time odometry requirements. Consequently, many algorithms have turned to feature extraction approaches. Nonetheless, with the advent of diverse scanning mode LiDARs, there arises a necessity to devise unique methods tailored to these sensors to facilitate algorithm migration. To address this challenge, we propose a weighted point-to-plane matching strategy that focuses on local details without relying on feature extraction. This improved approach mitigates the impact of imperfect plane fitting on localization accuracy. Moreover, we present a classification optimization method based on the normal vectors of planes to further refine algorithmic efficiency. Finally, we devise a tightly coupled LiDAR-inertial odometry system founded upon optimization schemes. Notably, we pioneer the derivation of an online gravity estimation method from the perspective of S 2 manifold optimization, effectively minimizing the influence of gravity estimation errors introduced during the initialization phase on localization accuracy. The efficacy of the proposed method was validated through experimentation employing various LiDAR sensors. The outcomes of indoor and outdoor experiments substantiate its capability to furnish real-time and precise localization and mapping results. [ABSTRACT FROM AUTHOR]

This article addresses the important problem of tilt measurement and stabilization. This is particularly important in the case of drone stabilization and navigation in underwater environments, multibeam sonar mapping, aerial photogrammetry in densely urbanized areas, etc. The tilt measurement process involves the fusion of information from at least two different sensors. Inertial sensors (IMUs) are unique in this context because they are both autonomous and passive at the same time and are therefore very attractive. Their calibration and systematic errors or bias are known problems, briefly discussed in the article due to their importance, and are relatively simple to solve. However, problems related to the accumulation of these errors over time and their autonomous and dynamic correction remain. This article proposes a solution to the problem of IMU tilt calibration, i.e., the pitch and roll and the accelerometer bias correction in dynamic conditions, and presents the process of calculating these parameters based on combined accelerometer and gyroscope records using a new approach based on measuring increments or differences in tilt measurement. Verification was performed by simulation under typical conditions and for many different inertial units, i.e., IMU devices, which brings the proposed method closer to the real application context. The article also addresses, to some extent, the issue of navigation, especially in the context of dead reckoning. [ABSTRACT FROM AUTHOR]

As the demand for spatial positioning continues to grow, positioning methods based on inertial measurement units (IMUs) are emerging as a promising research topic due to their low cost and robustness against environmental interference. These methods are particularly well suited for global navigation satellite system (GNSS)-denied environments and challenging visual scenarios. While existing algorithms for position estimation using IMUs have demonstrated some effectiveness, there is still significant room for improvement in terms of estimation accuracy. Current approaches primarily treat IMU data as simple time series, neglecting the frequency-domain characteristics of IMU signals. This paper emphasizes the importance of frequency-domain information in IMU signals and proposes a novel neural network, WINNet (Wavelet Inertial Neural Network), which integrates time- and frequency-domain signals using a wavelet transform for spatial positioning with inertial sensors. Additionally, we collected ground-truth data using a LiDAR setup and combined it with the TLIO dataset to form a new IMU spatial positioning dataset. The experimental results demonstrate that our proposed method outperforms the current state-of-the-art inertial neural network algorithms in terms of the ATE, RTE, and drift error metrics overall. [ABSTRACT FROM AUTHOR]

Lane-level localization is critical for autonomous vehicles (AVs). However, complex urban scenarios, particularly tunnels, pose significant challenges to AVs' localization systems. In this paper, we propose a fusion localization method that integrates multiple mass-production sensors, including Global Navigation Satellite Systems (GNSSs), Inertial Measurement Units (IMUs), cameras, and high-definition (HD) maps. Firstly, we use a novel electronic horizon module to assess GNSS integrity and concurrently load the HD map data surrounding the AVs. This map data are then transformed into a visual space to match the corresponding lane lines captured by the on-board camera using an improved BiSeNet. Consequently, the matched HD map data are used to correct our localization algorithm, which is driven by an extended Kalman filter that integrates multiple sources of information, encompassing a GNSS, IMU, speedometer, camera, and HD maps. Our system is designed with redundancy to handle challenging city tunnel scenarios. To evaluate the proposed system, real-world experiments were conducted on a 36-kilometer city route that includes nine consecutive tunnels, totaling near 13 km and accounting for 35% of the entire route. The experimental results reveal that 99% of lateral localization errors are less than 0.29 m, and 90% of longitudinal localization errors are less than 3.25 m, ensuring reliable lane-level localization for AVs in challenging urban tunnel scenarios. [ABSTRACT FROM AUTHOR]

With the development of simultaneous positioning and mapping technology in the field of automatic driving, the current simultaneous localization and mapping scheme is no longer limited to a single sensor and is developing in the direction of multi-sensor fusion to enhance the robustness and accuracy. In this study, a localization and mapping scheme named LVI-fusion based on multi-sensor fusion of camera, lidar and IMU is proposed. Different sensors have different data acquisition frequencies. To solve the problem of time inconsistency in heterogeneous sensor data tight coupling, the time alignment module is used to align the time stamp between the lidar, camera and IMU. The image segmentation algorithm is used to segment the dynamic target of the image and extract the static key points. At the same time, the optical flow tracking based on the static key points are carried out and a robust feature point depth recovery model is proposed to realize the robust estimation of feature point depth. Finally, lidar constraint factor, IMU pre-integral constraint factor and visual constraint factor together construct the error equation that is processed with a sliding window-based optimization module. Experimental results show that the proposed algorithm has competitive accuracy and robustness. [ABSTRACT FROM AUTHOR]

The Mobile Mapping System (MMS) plays a crucial role in generating accurate 3D maps for a wide range of applications. However, traditional MMS that utilizes tilted LiDAR (light detection and ranging) faces limitations in capturing comprehensive environmental data. We propose the "PVL-Cartographer" SLAM (Simultaneous Localization And Mapping) approach for MMS to address these limitations. This proposed system incorporates multiple sensors to yield dependable and precise mapping and localization. It consists of two subsystems: early fusion and intermediate fusion. In early fusion, range maps are created from LiDAR points within a panoramic image space, simplifying the integration of visual features. The SLAM system accommodates both visual features with and without augmented ranges. In intermediate fusion, camera and LiDAR nodes are merged using a pose graph, with constraints between nodes derived from IMU (Inertial Measurement Unit) data. Comprehensive testing in challenging outdoor settings demonstrates that the proposed SLAM system can generate trustworthy outcomes even in feature-scarce environments. Ultimately, our suggested PVL-Cartographer system effectively and accurately addresses the MMS localization and mapping challenge. [ABSTRACT FROM AUTHOR]

Removing moving objects from 3D LiDAR data plays a crucial role in advancing real-time odometry, life-long SLAM, and motion planning for robust autonomous navigation. In this paper, we present a novel method aimed at addressing the challenges faced by existing approaches when dealing with scenarios involving significant registration errors. The proposed approach offers a unique solution for removing moving objects without the need for registration, leveraging range flow estimation combined with IMU measurements. To this end, our method performs global range flow estimation by utilizing geometric constraints based on the spatio-temporal gradient information derived from the range image, and we introduce IMU measurements to further enhance the accuracy of range flow estimation. Through extensive quantitative evaluations, our approach showcases an improved performance, with an average mIoU of 45.8%, surpassing baseline methods such as Removert (43.2%) and Peopleremover (32.2%). Specifically, it exhibits a substantial improvement in scenarios characterized by a deterioration in registration performance. Moreover, our method does not rely on costly annotations, which make it suitable for SLAM systems with different sensor setups. [ABSTRACT FROM AUTHOR]

Real-time 3D scene reconstruction has attracted a great amount of attention in the fields of augmented reality, virtual reality and robotics. Previous works usually assumed slow sensor motions to avoid large interframe differences and strong image blur, but this limits the applicability of the techniques in real cases. In this study, we propose an end-to-end 3D reconstruction system that combines color, depth and inertial measurements to achieve a robust reconstruction with fast sensor motions. We involved an extended Kalman filter (EKF) to fuse RGB-D-IMU data and jointly optimize feature correspondences, camera poses and scene geometry by using an iterative method. A novel geometry-aware patch deformation technique is proposed to adapt the changes in patch features in the image domain, leading to highly accurate feature tracking with fast sensor motions. In addition, we maintained the global consistency of the reconstructed model by achieving loop closure with submap-based depth image encoding and 3D map deformation. The experiments revealed that our patch deformation method improves the accuracy of feature tracking, that our improved loop detection method is more efficient than the original method and that our system possesses superior 3D reconstruction results compared with the state-of-the-art solutions in handling fast camera motions. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) capability in smartphones has seen significant upgrades over the years. The latest ultra-low-cost GNSS receivers are capable of carrier-phase tracking and multi-constellation, dual-frequency signal reception. However, due to the limitations of these ultra-low-cost receivers and antennas, smartphone GNSS position solutions suffer significantly from urban multipath, poor signal reception, and signal blockage. This paper presents a novel sensor fusion technique using Precise Point Positioning (PPP) and the inertial sensors in smartphones, combined with a single- and dual-frequency (SFDF) optimisation scheme for smartphones. The smartphone is field-tested while attached to a vehicle's dashboard and is driven in multiple real-world situations. A total of five vehicle experiments were conducted and the solutions show that SFDF-PPP outperforms single-frequency PPP (SF-PPP) and dual-frequency PPP (DF-PPP). Solutions can be further improved by integrating with native smartphone IMU measurements and provide consistent horizontal positioning accuracy of <2 m rms through a variety obstructions. These results show a significant improvement from the existing literature using similar hardware in challenging environments. Future work will improve optimising inertial sensor calibration and integrate additional sensors. [ABSTRACT FROM AUTHOR]

Integration of the Global Navigation Satellite System (GNSS), with Inertial Measurement Unit (IMU) sensors to improve navigation performance, is widely used in many land-based applications. However, further application, especially in urban areas, is limited by the quality (due mainly to multipath effects) and availability of GNSS measurements, with a significant impact on performance, especially from low grade integration. To maximize the potential of GNSS measurements, this paper proposes a dual w-test-based quality control algorithm for integrated IMU/GNSS navigation in urban areas. Quality control is achieved through fault detection and exclusion (FDE) with the capability to detect simultaneous multiple faults in measurements from different satellites. The remaining fault-free GNSS measurements are fused with IMU sensor measurements to obtain the final improved state solution. The effectiveness of the algorithm is validated in a deep urban field test. Compared to the cases without fault exclusion, the results show improvements of about 24% and 30% in horizontal and vertical positioning components, respectively. [ABSTRACT FROM AUTHOR]

The satellite gravity mission GRACE(-FO) has not yet reached its designed baseline accuracy. Previous studies demonstrated that the deficiency in the sensor system or the related signal processing might be responsible, which in turn motivates us to keep revising the sensor data processing, typically the spacecraft’s attitude. Many efforts in the past have been made to enhance the attitude modeling for GRACE, for instance, the latest release reprocesses the attitude by fusing the angular acceleration with the star camera/tracker (SC) measurements, which helps to reduce the error in Level-2 temporal gravity fields. Therefore, in addition to GRACE, revising GRACE-FO attitude determination might make sense as well. This study starts with the most original raw GRACE-FO Level-1A data including those from three SCs and one IMU (Inertial Measurement Unit) sensors, and manage to generate a new publicly available Level-1B attitude product called HUGG-01 covering from June 2018 to December 2020, using our independently-developed software. The detailed treatment of individual payload is present in this study, and an indirect Kalman filter method is introduced to fuse the multiple sensors to acquire a relatively stable and precise attitude estimation. Unlike the direct SC combination method with a predefined weight as recommended in previous work, we propose an involvement of each SC measurement in the Kalman filter to enable a dynamic weight adjustment. Intensive experiments are further carried out to assess the HUGG-01, which demonstrate that the error level of HUGG-01 is entirely within the design requirement, i.e., the resulting KBR pointing variations are well controlled within 1 mrad (pitch), 5 mrad (roll) and 1 mrad (yaw). Moreover, comparisons with the official JPL-V04 attitude product demonstrate an equivalent performance in the low-to-middle spectrum, with even a slightly lower noise level (in the high spectrum) than JPL-V04. Further analysis on KBR range-rate residuals and gravity recovery on January 2019 indicates that, i.e., RMS of the difference (HUGG-01 minus JPL-V04) for the range rate is less than 3.234×10−8 m/s, and the amplitude of geoid height difference is approximately 0.5 cm. Both differences are below the sensitivity of the state-of-the-art satellite gravity mission, demonstrating a good agreement between HUGG-01 and JPL-V04.

This paper presents a solution to the problem of simultaneous localization and mapping (SLAM), developed from a particle filter, utilizing a monocular camera as its main sensor. It implements a novel sample-weighting idea, based on the of sorting of particles into sets and separating those sets with an importance-factor offset. The grouping criteria for samples is the number of landmarks correctly matched by a given particle. This results in the stratification of samples and amplifies weighted differences. The proposed system is designed for a UAV, navigating outdoors, with a downward-pointed camera. To evaluate the proposed method, it is compared with different samples-weighting approaches, using simulated and real-world data. The conducted experiments show that the developed SLAM solution is more accurate and robust than other particle-filter methods, as it allows the employment of a smaller number of particles, lowering the overall computational complexity.

Simultaneous Localization and Mapping (SLAM) has always been the focus of the robot navigation for many decades and becomes a research hotspot in recent years. Because a SLAM system based on vision sensor is vulnerable to environment illumination and texture, the problem of initial scale ambiguity still exists in a monocular SLAM system. The fusion of a monocular camera and an inertial measurement unit (IMU) can effectively solve the scale blur problem, improve the robustness of the system, and achieve higher positioning accuracy. Based on a monocular visual-inertial navigation system (VINS-mono), a state-of-the-art fusion performance of monocular vision and IMU, this paper designs a new initialization scheme that can calculate the acceleration bias as a variable during the initialization process so that it can be applied to low-cost IMU sensors. Besides, in order to obtain better initialization accuracy, visual matching positioning method based on feature point is used to assist the initialization process. After the initialization process, it switches to optical flow tracking visual positioning mode to reduce the calculation complexity. By using the proposed method, the advantages of feature point method and optical flow method can be fused. This paper, the first one to use both the feature point method and optical flow method, has better performance in the comprehensive performance of positioning accuracy and robustness under the low-cost sensors. Through experiments conducted with the EuRoc dataset and campus environment, the results show that the initial values obtained through the initialization process can be efficiently used for launching nonlinear visual-inertial state estimator and positioning accuracy of the improved VINS-mono has been improved by about 10% than VINS-mono.

IMU preintegration technology has been widely used in the optimization-based sensor fusion framework, in order to avoid reintegrating the high-frequency IMU measurements at each iteration and maintain the ability of bias correction when bias estimation changes. Since IMU preintegration technology was first proposed, several improved versions have been designed by changing the attitude parameterization or the numerical integration method in the most current related research. However, all of these versions have failed to take the change of gravity and the earth rotation into consideration. In this paper, we redesign the IMU preintegration algorithm in which the earth rotation and gravity vector are calculated from the geodetic position. Compared with the covariance matrix form, in this paper, the uncertainty of the preintegrated IMU measurements is propagated in the form of a square root information matrix (SRIM) for better numerical stability and easy use in the optimization-based framework. We evaluate the improved IMU preintegration algorithm by using the dataset collected by our sensor platform equipped with two different-grade IMUs. The test results show that the improved IMU preintegration algorithm can cope well with the gravity change and earth rotation. The earth rotation must be taken into consideration for the high-grade IMU that can effectively sense the earth rotation. If the change of gravity is omitted, the root-mean-square error (RMSE) of the horizontal attitude is about 1.38 times greater than the geodetic displacement. Additionally, the positioning RMSE does not increase obviously within a limited range, which means tens of kilometers and several hundred meters for the low-grade and high-grade IMU used in the experiment, respectively.

Simultaneous localization and mapping (SLAM) of a monocular projective camera installed on an unmanned aerial vehicle (UAV) is a challenging task in photogrammetry, computer vision, and robotics. This paper presents a novel real-time monocular SLAM solution for UAV applications. It is based on two steps: consecutive construction of the UAV path, and adjacent strip connection. Consecutive construction rapidly estimates the UAV path by sequentially connecting incoming images to a network of connected images. A multilevel pyramid matching is proposed for this step that contains a sub-window matching using high-resolution images. The sub-window matching increases the frequency of tie points by propagating locations of matched sub-windows that leads to a list of high-frequency tie points while keeping the execution time relatively low. A sparse bundle block adjustment (BBA) is employed to optimize the initial path by considering nuisance parameters. System calibration parameters with respect to global navigation satellite system (GNSS) and inertial navigation system (INS) are optionally considered in the BBA model for direct georeferencing. Ground control points and checkpoints are optionally included in the model for georeferencing and quality control. Adjacent strip connection is enabled by an overlap analysis to further improve connectivity of local networks. A novel angular parametrization based on spherical rotation coordinate system is presented to address the gimbal lock singularity of BBA. Our results suggest that the proposed scheme is a precise real-time monocular SLAM solution for a UAV.

Global Navigation Satellite Systems (GNSSs) are commonly used for positioning vehicles in open areas. Yet a GNSS frequently encounters loss of lock in urban areas. This paper presents a new real-time localization system using measurements from vehicle odometer data and data from an onboard inertial measurement unit (IMU), in the case of lacking GNSS information. A Dead Reckoning model integrates odometer data, IMU angular and velocity data to estimate the rough position of the vehicle. We then use an R-Tree structured reference road map of pitch data to boost spatial search efficiency. An optimized time series subsequence matching method matches the measured pitch data and the stored pitch data in reference road map for more accurate position estimation. The two estimated positions are fused using an extended Kalman filter model for final localization. The proposed localization system was tested for computational complexity with a median runtime of 12 ms, and for positioning accuracy with a median position error of 0.3 m.

Modern smartphones contain embedded global navigation satellite systems (GNSSs), inertial measurement units (IMUs), cameras, and other sensors which are capable of providing user position, velocity, and attitude. However, it is difficult to utilize the actual navigation performance capabilities of smartphones due to the low-cost and disparate sensors, software technologies adopted by manufacturers, and the significant influence of environmental conditions. In this study, we proposed a scheme that integrated sensor data from smartphone IMUs, GNSS chipsets, and cameras using an extended Kalman filter (EKF) to enhance the navigation performance. The visual data from the camera was preprocessed using oriented FAST (Features from accelerated segment test) and rotated BRIEF (Binary robust independent elementary features)-simultaneous localization and mapping (ORB-SLAM), rescaled by applying GNSS measurements, and converted to velocity data before being utilized to update the integration filter. In order to verify the performance of the integrated system, field test data was collected in a downtown area of Tainan City, Taiwan. Experimental results indicated that visual data contributed significantly to improving the accuracy of the navigation performance, demonstrating improvements of 43.0% and 51.3% in position and velocity, respectively. It was verified that the proposed integrated system, which used data from smartphone sensors, was efficient in terms of increasing navigation accuracy in GNSS-challenging environments.

The satellite gravity mission GRACE(-FO) has not yet reached its designed baseline accuracy. Previous studies demonstrated that the deficiency in the sensor system or the related signal processing might be responsible, which in turn motivates us to keep revising the sensor data processing, typically the spacecraft's attitude. Many efforts in the past have been made to enhance the attitude modeling for GRACE, for instance, the latest release reprocesses the attitude by fusing the angular acceleration with the star camera/tracker (SC) measurements, which helps to reduce the error in Level-2 temporal gravity fields. Therefore, in addition to GRACE, revising GRACE-FO attitude determination might make sense as well. This study starts with the most original raw GRACE-FO Level-1A data including those from three SCs and one IMU (Inertial Measurement Unit) sensors, and manage to generate a new publicly available Level-1B attitude product called HUGG-01 covering from June 2018 to December 2020, using our independently-developed software. The detailed treatment of individual payload is present in this study, and an indirect Kalman filter method is introduced to fuse the multiple sensors to acquire a relatively stable and precise attitude estimation. Unlike the direct SC combination method with a predefined weight as recommended in previous work, we propose an involvement of each SC measurement in the Kalman filter to enable a dynamic weight adjustment. Intensive experiments are further carried out to assess the HUGG-01, which demonstrate that the error level of HUGG-01 is entirely within the design requirement, i.e., the resulting KBR pointing variations are well controlled within 1 mrad (pitch), 5 mrad (roll) and 1 mrad (yaw). Moreover, comparisons with the official JPL-V04 attitude product demonstrate an equivalent performance in the low-to-middle spectrum, with even a slightly lower noise level (in the high spectrum) than JPL-V04. Further analysis on KBR range-rate residuals and gravity recovery on January 2019 indicates that, i.e., RMS of the difference (HUGG-01 minus JPL-V04) for the range rate is less than 3.234 × 10 − 8 m/s, and the amplitude of geoid height difference is approximately 0.5 cm. Both differences are below the sensitivity of the state-of-the-art satellite gravity mission, demonstrating a good agreement between HUGG-01 and JPL-V04. [ABSTRACT FROM AUTHOR]

This paper presents a solution to the problem of simultaneous localization and mapping (SLAM), developed from a particle filter, utilizing a monocular camera as its main sensor. It implements a novel sample-weighting idea, based on the of sorting of particles into sets and separating those sets with an importance-factor offset. The grouping criteria for samples is the number of landmarks correctly matched by a given particle. This results in the stratification of samples and amplifies weighted differences. The proposed system is designed for a UAV, navigating outdoors, with a downward-pointed camera. To evaluate the proposed method, it is compared with different samples-weighting approaches, using simulated and real-world data. The conducted experiments show that the developed SLAM solution is more accurate and robust than other particle-filter methods, as it allows the employment of a smaller number of particles, lowering the overall computational complexity. [ABSTRACT FROM AUTHOR]

Registration of long-wave infrared (LWIR) hyperspectral images with their thermal and emissivity components has until now received comparatively less attention with respect to the visible near and short wave infrared hyperspectral images. In this paper, the registration of LWIR hyperspectral images is investigated to enhance applications of LWIR images such as change detection, temperature and emissivity separation, and target detection. The proposed approach first searches for the best features of hyperspectral image pixels for extraction and matching in the LWIR range and then performs a global registration over two-dimensional maps of three-dimensional hyperspectral cubes. The performances of temperature and emissivity features in the thermal domain along with the average energy and principal components of spectral radiance are investigated. The global registration performed over whole 2D maps is further improved by blockwise local refinements. Among the two proposed approaches, the geometric refinement seeks the best keypoint combination in the neighborhood of each block to estimate the transformation for that block. The alternative optimization-based refinement iteratively finds the best transformation by maximizing the similarity of the reference and transformed blocks. The possible blocking artifacts due to blockwise mapping are finally eliminated by pixelwise refinement. The experiments are evaluated with respect to the (i) utilized similarity metrics in the LWIR range between transformed and reference blocks, (ii) proposed geometric- and optimization-based methods, and (iii) image pairs captured on the same and different days. The better performance of the proposed approach compared to manual, GPU-IMU-based, and state-of-the-art image registration methods is verified. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) has always been the focus of the robot navigation for many decades and becomes a research hotspot in recent years. Because a SLAM system based on vision sensor is vulnerable to environment illumination and texture, the problem of initial scale ambiguity still exists in a monocular SLAM system. The fusion of a monocular camera and an inertial measurement unit (IMU) can effectively solve the scale blur problem, improve the robustness of the system, and achieve higher positioning accuracy. Based on a monocular visual-inertial navigation system (VINS-mono), a state-of-the-art fusion performance of monocular vision and IMU, this paper designs a new initialization scheme that can calculate the acceleration bias as a variable during the initialization process so that it can be applied to low-cost IMU sensors. Besides, in order to obtain better initialization accuracy, visual matching positioning method based on feature point is used to assist the initialization process. After the initialization process, it switches to optical flow tracking visual positioning mode to reduce the calculation complexity. By using the proposed method, the advantages of feature point method and optical flow method can be fused. This paper, the first one to use both the feature point method and optical flow method, has better performance in the comprehensive performance of positioning accuracy and robustness under the low-cost sensors. Through experiments conducted with the EuRoc dataset and campus environment, the results show that the initial values obtained through the initialization process can be efficiently used for launching nonlinear visual-inertial state estimator and positioning accuracy of the improved VINS-mono has been improved by about 10% than VINS-mono. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) of a monocular projective camera installed on an unmanned aerial vehicle (UAV) is a challenging task in photogrammetry, computer vision, and robotics. This paper presents a novel real-time monocular SLAM solution for UAV applications. It is based on two steps: consecutive construction of the UAV path, and adjacent strip connection. Consecutive construction rapidly estimates the UAV path by sequentially connecting incoming images to a network of connected images. A multilevel pyramid matching is proposed for this step that contains a sub-window matching using high-resolution images. The sub-window matching increases the frequency of tie points by propagating locations of matched sub-windows that leads to a list of high-frequency tie points while keeping the execution time relatively low. A sparse bundle block adjustment (BBA) is employed to optimize the initial path by considering nuisance parameters. System calibration parameters with respect to global navigation satellite system (GNSS) and inertial navigation system (INS) are optionally considered in the BBA model for direct georeferencing. Ground control points and checkpoints are optionally included in the model for georeferencing and quality control. Adjacent strip connection is enabled by an overlap analysis to further improve connectivity of local networks. A novel angular parametrization based on spherical rotation coordinate system is presented to address the gimbal lock singularity of BBA. Our results suggest that the proposed scheme is a precise real-time monocular SLAM solution for a UAV. [ABSTRACT FROM AUTHOR]

IMU preintegration technology has been widely used in the optimization-based sensor fusion framework, in order to avoid reintegrating the high-frequency IMU measurements at each iteration and maintain the ability of bias correction when bias estimation changes. Since IMU preintegration technology was first proposed, several improved versions have been designed by changing the attitude parameterization or the numerical integration method in the most current related research. However, all of these versions have failed to take the change of gravity and the earth rotation into consideration. In this paper, we redesign the IMU preintegration algorithm in which the earth rotation and gravity vector are calculated from the geodetic position. Compared with the covariance matrix form, in this paper, the uncertainty of the preintegrated IMU measurements is propagated in the form of a square root information matrix (SRIM) for better numerical stability and easy use in the optimization-based framework. We evaluate the improved IMU preintegration algorithm by using the dataset collected by our sensor platform equipped with two different-grade IMUs. The test results show that the improved IMU preintegration algorithm can cope well with the gravity change and earth rotation. The earth rotation must be taken into consideration for the high-grade IMU that can effectively sense the earth rotation. If the change of gravity is omitted, the root-mean-square error (RMSE) of the horizontal attitude is about 1.38 times greater than the geodetic displacement. Additionally, the positioning RMSE does not increase obviously within a limited range, which means tens of kilometers and several hundred meters for the low-grade and high-grade IMU used in the experiment, respectively. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSSs) are commonly used for positioning vehicles in open areas. Yet a GNSS frequently encounters loss of lock in urban areas. This paper presents a new real-time localization system using measurements from vehicle odometer data and data from an onboard inertial measurement unit (IMU), in the case of lacking GNSS information. A Dead Reckoning model integrates odometer data, IMU angular and velocity data to estimate the rough position of the vehicle. We then use an R-Tree structured reference road map of pitch data to boost spatial search efficiency. An optimized time series subsequence matching method matches the measured pitch data and the stored pitch data in reference road map for more accurate position estimation. The two estimated positions are fused using an extended Kalman filter model for final localization. The proposed localization system was tested for computational complexity with a median runtime of 12 ms, and for positioning accuracy with a median position error of 0.3 m. [ABSTRACT FROM AUTHOR]

Modern smartphones contain embedded global navigation satellite systems (GNSSs), inertial measurement units (IMUs), cameras, and other sensors which are capable of providing user position, velocity, and attitude. However, it is difficult to utilize the actual navigation performance capabilities of smartphones due to the low-cost and disparate sensors, software technologies adopted by manufacturers, and the significant influence of environmental conditions. In this study, we proposed a scheme that integrated sensor data from smartphone IMUs, GNSS chipsets, and cameras using an extended Kalman filter (EKF) to enhance the navigation performance. The visual data from the camera was preprocessed using oriented FAST (Features from accelerated segment test) and rotated BRIEF (Binary robust independent elementary features)-simultaneous localization and mapping (ORB-SLAM), rescaled by applying GNSS measurements, and converted to velocity data before being utilized to update the integration filter. In order to verify the performance of the integrated system, field test data was collected in a downtown area of Tainan City, Taiwan. Experimental results indicated that visual data contributed significantly to improving the accuracy of the navigation performance, demonstrating improvements of 43.0% and 51.3% in position and velocity, respectively. It was verified that the proposed integrated system, which used data from smartphone sensors, was efficient in terms of increasing navigation accuracy in GNSS-challenging environments. [ABSTRACT FROM AUTHOR]