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In this paper, we present a system for estimating the trajectory of a moving RGB-D camera with applications to building maps of large indoor environments. Unlike the current most researches, we propose a 'feature model' based RGB-D visual odometry system for a computationally-constrained mobile platform, where the 'feature model' is persistent and dynamically updated from new observations using a Kalman filter. In this paper, we firstly propose a mixture of Gaussians model for the depth random noise estimation, which is used to describe the spatial uncertainty of the feature point cloud. Besides, we also introduce a general depth calibration method to remove systematic errors in the depth readings of the RGB-D camera. We provide comprehensive theoretical and experimental analysis to demonstrate that our model based iterative-closest-point (ICP) algorithm can achieve much higher localization accuracy compared to the conventional ICP. The visual odometry runs at frequencies of 30 Hz or higher, on VGA images, in a single thread on a desktop CPU with no GPU acceleration required. Finally, we examine the problem of place recognition from RGB-D images, in order to form a pose-graph SLAM approach to refining the trajectory and closing loops. We evaluate the effectiveness of the system on using publicly available datasets with ground-truth data. The entire system is available for free and open-source online. [ABSTRACT FROM AUTHOR]

This paper presents the advantages of a single-camera stereo omnidirectional system (SOS) in estimating egomotion in real-world environments. The challenge of applying omnidirectional stereo vision via a single camera is what separates our work from others. In practice, dynamic environments, deficient illumination, and poor textured surfaces result in the lack of features to track in the observable scene. As a consequence, this negatively affects the pose estimation of visual odometry systems, regardless of their field of view. We compare the tracking accuracy and stability of the single-camera SOS versus an RGB-D device under various real circumstances. Our quantitative evaluation is performed with respect to 3D ground-truth data obtained from a motion capture system. The datasets and experimental results we provide are unique due to the nature of our catadioptric omnistereo rig, and the situations in which we captured these motion sequences. We have implemented a tracking system with simple rules applicable to both synthetic and real scenes. Our implementation does not make any motion model assumptions, and it maintains a fixed configuration among the compared sensors. Our experimental outcomes confer the robustness in 3D metric visual odometry estimation that the single-camera SOS can achieve under normal and special conditions in which other perspective narrow view systems such as RGB-D cameras would fail. [ABSTRACT FROM AUTHOR]

Google's Project Tango has made integrated depth sensing and onboard visual-intertial odometry available to mobile devices such as phones and tablets. In this work, we explore the problem of large-scale, real-time 3D reconstruction on a mobile devices of this type. Solving this problem is a necessary prerequisite for many indoor applications, including navigation, augmented reality and building scanning. The main challenges include dealing with noisy and low-frequency depth data and managing limited computational and memory resources. State of the art approaches in large-scale dense reconstruction require large amounts of memory and high-performance GPU computing. Other existing 3D reconstruction approaches on mobile devices either only build a sparse reconstruction, offload their computation to other devices, or require long post-processing to extract the geometric mesh. In contrast, we can reconstruct and render a global mesh on the fly, using only the mobile device's CPU, in very large (300 m $$^2$$ ) scenes, at a resolutions of 2-3 cm. To achieve this, we divide the scene into spatial volumes indexed by a hash map. Each volume contains the truncated signed distance function for that area of space, as well as the mesh segment derived from the distance function. This approach allows us to focus computational and memory resources only in areas of the scene which are currently observed, as well as leverage parallelization techniques for multi-core processing. Furthermore, we describe an on-device post-processing method for fusing datasets from multiple, independent trials, in order to improve the quality and coverage of the reconstruction. We discuss how the particularities of the devices impact our algorithm and implementation decisions. Finally, we provide both qualitative and quantitative results on publicly available RGB-D datasets, and on datasets collected in real-time from two devices. [ABSTRACT FROM AUTHOR]

This paper investigates the performance-complexity tradeoff of the wide-sense likelihood ascent search (WSLAS) detectors in large multicode sparse-sequence CDMA. It is illustrated that when each sequence has sparsely only 16 nonzero chips, in a channel load up to 1.05 bits/s/Hz and a broad SNR region, the linearly complex WSLAS detectors can achieve the benchmark optimum BER while the complexity is significantly reduced from 0.5 times bit number to a constant less than 30 additions per bit by the sequence sparsity. The evaluation result of multiuse efficiency also shows that the sparse sequences of 16 nonzero chips can already provide a sufficient degree of freedom. [ABSTRACT FROM AUTHOR]

This paper presents an open-source indoor navigation system for quadrotor micro aerial vehicles (MAVs), implemented in the ROS framework. The system requires a minimal set of sensors including a planar laser range-finder and an inertial measurement unit. We address the issues of autonomous control, state estimation, path-planning, and teleoperation, and provide interfaces that allow the system to seamlessly integrate with existing ROS navigation tools for 2D SLAM and 3D mapping. All components run in real time onboard the MAV, with state estimation and control operating at 1 kHz. A major focus in our work is modularity and abstraction, allowing the system to be both flexible and hardware-independent. All the software and hardware components which we have developed, as well as documentation and test data, are available online. [ABSTRACT FROM AUTHOR]

We design a novel 'folded' spherical catadioptric rig (formed by two coaxially-aligned spherical mirrors of distinct radii and a single perspective camera) to recover near-spherical range panoramas (about 360° × 153°) from the fusion of depth given by optical flow and stereoscopy. We observe that for rigid motion that is parallel to a plane, optical flow and stereo generate nearly complementary distributions of depth resolution. While optical flow provides strong depth cues in the periphery and near the poles of the view-sphere, stereo generates reliable depth in a narrow band about the equator instead. We exploit this dual-modality principle by modeling (separately) the depth resolution of optical flow and stereo in order to fuse them later on a probabilistic spherical panorama. We achieve a desired vertical field-of-view and optical resolution by deriving a linearized model of the rig in terms of three parameters (radii of the two mirrors plus axial distance between the mirrors' centers). We analyze the error due to the violation of the single viewpoint constraint and formulate additional constraints on the design to minimize this error. We evaluate our proposed method via a synthetic model and with real-world prototypes by computing dense spherical panoramas of depth from cluttered indoor environments after fusing the two modalities (stereo and optical flow). [ABSTRACT FROM AUTHOR]