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While visual Simultaneous Localization and Mapping systems are well studied and achieve impressive results in indoor and urban settings, natural, outdoor, and open‐field environments are much less explored and still present relevant research challenges. Visual navigation and local mapping have shown a relatively good performance in open‐field environments. However, globally consistent mapping and long‐term localization still depend on the robustness of loop detection and closure, for which the literature is scarce. In this work, we propose a novel method to pave the way towards robust loop detection in open fields, particularly in agricultural settings, based on local feature search and stereo geometric refinement, with a final stage of relative pose estimation. Our method consistently achieves good loop detections, with a median error of 15 cm. We aim to characterize open fields as a novel environment for loop detection, understanding the limitations and problems that arise when dealing with them. Code is available at: https://github.com/CIFASIS/StereoLoopDetector [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of agricultural intelligence, the application of intelligent agricultural robots in orchard management has been widely concerned. However, the complex environment in apple orchards, such as the occlusion of branches and leaves, dynamic changes and differences in lighting conditions, poses great challenges to the positioning accuracy and map construction ability of synchronous positioning and map construction technology. In order to solve the problem that existing synchronous localization and map construction techniques are not accurate enough in this kind of environment, a loopback detection algorithm based on neighborhood search and scanning context fusion is proposed. The method realizes rough loop frame search through neighborhood search to improve speed, and global precise search combined with scanning context to improve accuracy. In the loop frame matching, an optimization algorithm combining normal distribution transformation and iterative nearest point is used to reduce the cumulative error significantly. According to the environmental characteristics of apple orchard, the research preprocessed the point cloud data collected by LiDAR, and combined with the LiDAR odometer method based on line and plane feature point matching, the pose estimation and map construction were carried out. The experimental results show that compared to the independent use of Radius Search and Scan Context, the proposed algorithm increases the number of loopback frames by 50.19% and 7.23%, respectively, and reduces the root mean square error by 28.31% and 45.60%, respectively. The research results significantly improve the positioning and mapping performance of synchronous positioning and map construction technology in the complex environment of apple orchards, and provide technical support for the application of intelligent agricultural robots in orchard management.

Loop closure is the key to eliminate the cumulative error in simultaneous localization and mapping (SLAM) system. When the illumination conditions or the viewing angle change greatly, the traditional appearance-based loop closure methods often fail. Aiming at this situation, an object-level loop closure method is proposed based on ORBSLAM2 framework. Firstly, semantic information obtained by object detection and the feature point information are used to construct object-level semantic map. Then semantic map is abstracted into topological map and landmarks into nodes, node information is described by color histogram, and combined geometric relationship between nodes, a graph matching method is proposed to realize loop detection based on semantic and geometric consistency constraints. When a loop is detected, the loop is corrected by object alignment. Finally, experiments are carried out on the published TUM and USTC data sets, and the results show that the accuracy of the proposed system is 49.58% higher than that of ORBSLAM2 on average, and the constructed semantic map shows a good positioning effect. [ABSTRACT FROM AUTHOR]

Point cloud-based place recognition plays a crucial role in robotics and unmanned vehicle tasks, particularly in relocalization and loop detection modules of LiDAR-based simultaneous localization and mapping systems. It’s also essential for global localization in scenarios where prior pose information is unavailable. However, three-dimensional point cloud data are characterized by sparsity and disorder, making it challenging to extract robust features. This study proposes an end-to-end deep learning network to compress the point cloud into a global descriptor for point cloud retrieval tasks. The proposed network implements two self-attention modules, i.e., the local point cloud-based self-attention and global point cloud-based self-attention mechanisms. Due to this two-stage self-attention mechanism, the proposed PointAttentionVLAD network achieved a higher average recall @ top 1 on the Benchmark datasets than the SOE-Net and LPD-Net algorithms by 0.39% and 3.41%, respectively. Furthermore, experiments were conducted on KAIST dataset to assess the generalization ability of the proposed PointAttentionVLAD, and the proposed network demonstrated impressive performance on KAIST dataset. The code and the pre-trained model of the proposed PointAttentionVLAD are available at https://github.com/leo6862/pointattentionvlad.

The planning of multi-branch cable harness layouts holds significant practical importance in aircraft industrial contexts, yet it has received limited attention in prior research. This study aims to address the matter concerning the significance of managing multiple constraints and preventing loops. It formulates the problem as an optimization problem in 3D free-form space and resolves it using an extended A* path planning approach in combination with the ant colony optimization algorithm. Initially, a feasible search space for wiring is established through the repair and simplification of the input CAD model. Subsequently, the topology of a multi-branched wiring harness is identified, taking into account industrial requirements related to cable physics, turning, support, bundling, and electromagnetic compatibility constraints. Specifically, the disassembly or merging of branches and loops is employed to avoid wire loops. Ultimately, we propose an A*–ant colony optimization algorithm (A*-ACO) with an enhanced heuristic function for neighboring points, incorporating a concentration increment model. Experimental tests illustrate the effectiveness of this approach in minimizing wire loops and reducing the total cable layout cost, considering factors such as length, bundling, and turning costs. It results in a reduction of 67.0%, 68.5%, and 51.1% compared to A*, ACO, and manual wiring methods, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Visual Place Recognition (VPR) is a fundamental yet challenging task in Visual Simultaneous Localization and Mapping (V-SLAM) problems. The VPR works as a subsystem of the V-SLAM. VPR is the task of retrieving images upon revisiting the same place in different conditions. The problem is even more difficult for agricultural and all-terrain autonomous mobile robots that work in different scenarios and weather conditions. Over the last few years, many state-of-the-art methods have been proposed to solve the limitations of existing VPR techniques. VPR using bag-of-words obtained from local features works well for a large-scale image retrieval problem. However, the aggregation of local features arbitrarily produces a large bag-of-words vector database, limits the capability of efficient feature learning, and aggregation and querying of candidate images. Moreover, aggregating arbitrary features is inefficient as not all local features equally contribute to long-term place recognition tasks. Therefore, a novel VPR architecture is proposed suitable for efficient place recognition with semantically meaningful local features and their 3D geometrical verifications. The proposed end-to-end architecture is fueled by a deep neural network, a bag-of-words database, and 3D geometrical verification for place recognition. This method is aware of meaningful and informative features of images for better scene understanding. Later, 3D geometrical information from the corresponding meaningful features is computed and utilised for verifying correct place recognition. The proposed method is tested on four well-known public datasets, and Micro Aerial Vehicle (MAV) recorded dataset for experimental validation from Victoria Park, Adelaide, Australia. The extensive experimental results considering standard evaluation metrics for VPR show that the proposed method produces superior performance than the available state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Place recognition and loop detection play an important role in the outdoor simultaneous localization and mapping (SLAM). In this paper, we present a place recognition and navigation method based on the random forest algorithm for outdoor mobile robot with three-dimensional (3D) laser point cloud data. The 3D point cloud-based place recognition employs a global feature extractor composed of well-designed geometric and statistical features without preprocessing for the effective training and construction of the random forest classifier. Then, the environment point cloud and node map are fed into the classifier for the place recognition task. The place recognition method is subsequently applied to the loop detection of the mobile robots. To begin with, the odometry pose nodes are sorted according to their location and distance, and are then fed into the random forest classifier for loop discrimination. Eventually, loop verification based on the overlap rate of two point clouds is performed to identify the true loop. The loop detection method is combined with S4-SLAM proposed earlier by us to form the new S4-SLAM2 algorithm. Node maps constructed via S4-SLAM2 perform global re-localization in the given map by combining the place recognition method and the point cloud registration. The proposed method was verified by extensive evaluations using the KITTI dataset, as well as real-world scenarios of outdoor environments. The loop detection recall was determined as 82%, with 100% precision. The S4-SLAM2 system also exhibited high localization and mapping accuracies, with a localization output rate of 10 Hz and an average localization drift lower than 1%. [ABSTRACT FROM AUTHOR]

We show that the forwarding strategies in the named data networking (NDN) architecture and the original content centric networking (CCN) architecture cannot ensure that Interests return the requested data objects when routing-table loops exist in a stable or dynamic network. We also show that no correct Interest forwarding strategy that allows Interest aggregation can be designed solely on the basis of identifying Interests uniquely in order to detect Interest loops. We introduce SIFAH (Strategy for Interest Forwarding and Aggregation with Hop-Counts).  SIFAH prevents or detects Interest loops when Interests are aggregated or forwarded over one or multiple paths. As a result, it is far more efficient than the forwarding strategy in NDN and the original CCN proposal. SIFAH operates by having forwarding information bases (FIB) store the next hops and number of hops to named content prefixes, and by using Interests that state the names of requested content and hop counts that reflect the information in their FIBs.

Visual Place Recognition (VPR) is a fundamental yet challenging task in Visual Simultaneous Localization and Mapping (V-SLAM) problems. The VPR works as a subsystem of the V-SLAM. VPR is the task of retrieving images upon revisiting the same place in different conditions. The problem is even more difficult for agricultural and all-terrain autonomous mobile robots that work in different scenarios and weather conditions. Over the last few years, many state-of-the-art methods have been proposed to solve the limitations of existing VPR techniques. VPR using bag-of-words obtained from local features works well for a large-scale image retrieval problem. However, the aggregation of local features arbitrarily produces a large bag-of-words vector database, limits the capability of efficient feature learning, and aggregation and querying of candidate images. Moreover, aggregating arbitrary features is inefficient as not all local features equally contribute to long-term place recognition tasks. Therefore, a novel VPR architecture is proposed suitable for efficient place recognition with semantically meaningful local features and their 3D geometrical verifications. The proposed end-to-end architecture is fueled by a deep neural network, a bag-of-words database, and 3D geometrical verification for place recognition. This method is aware of meaningful and informative features of images for better scene understanding. Later, 3D geometrical information from the corresponding meaningful features is computed and utilised for verifying correct place recognition. The proposed method is tested on four well-known public datasets, and Micro Aerial Vehicle (MAV) recorded dataset for experimental validation from Victoria Park, Adelaide, Australia. The extensive experimental results considering standard evaluation metrics for VPR show that the proposed method produces superior performance than the available state-of-the-art methods.

There are three main problems for theorem proving with a standard cut-free system for the first order minimal logic. The first problem is the possibility of looping. Secondly, it might generate proofs which are permutations of each other. Finally, during the proof some choice should be made to decide which rules to apply and where to use them. New systems with history mechanisms were introduced for solving the looping problems of automated theorem provers in the first order minimal logic. In order to solve the rule selection problem, recurrent neural networks are deployed and they are used to determine which formula from the context should be used on further steps. As a result, it yields to the reduction of time during theorem proving. [ABSTRACT FROM AUTHOR]

A globally consistent map is the basis of indoor robot localization and navigation. However, map built by Rao-Blackwellized Particle Filter (RBPF) doesn’t have high global consistency which is not suitable for long-term application in large scene. To address the problem, we present an improved RBPF Lidar SLAM system with loop detection and correction named LCPF. The efficiency and accuracy of loop detection depend on the segmentation of submaps. Instead of dividing the submap at fixed number of laser scan like existing method, Dynamic Submap Segmentation is proposed in LCPF. This segmentation algorithm decreases the error inside the submap by splitting the submap where there is high scan match error and later rectifies the error by an improved pose graph optimization between submaps. In order to segment the submap at appropriate point, when to create a new submap is determined by both the accumulation of scan match error and the particle distribution. Furthermore, LCPF uses branch and bound algorithm as basic detector for loop detection and multiple criteria to judge the reliability of a loop. In the criteria, a novel parameter called usable ratio was proposed to measure the useful information that a laser scan containing. Finally, comparisons to existing 2D-Lidar mapping algorithm are performed with a series of open dataset simulations and real robot experiments to demonstrate the effectiveness of LCPF.

For outdoor ground/watersurface multi-scene applications with sparse feature points, high moving speed and high dynamic noises, a real-time 3D LIDAR SLAM system (S4-SLAM) for unmanned vehicles/ships is proposed in this paper, which is composed of the odometry function in front-end and the loop closure function in back-end. Firstly, linear interpolation is used to eliminate the motion distortion caused by robot motions in the data pre-processing step. Two nodes are constructed in the odometry function: the localization node combines the improved Super4PCS with the standard ICP to realize a coarse-to-fine scan matching and outputs the location information of the robot at a high frequency (5 Hz); the correction node introduces a local map with dynamic voxel grid storage structure, which can accelerate the NDT(Normal Distributions Transform) matching process between key-frames and the local map, and then corrects the localization node at a low frequency (1 Hz) to obtain more accurate location information. In the loop closure function, a location-based loop detection approach is introduced and the overlap rate of point clouds is used to verify the loops, so that the global optimization can be carried out to obtain high-precision trajectory and map estimates. The proposed method has been extensively evaluated on the KITTI odometry benchmark and also tested in real-life campus and harbor environments. The results show that our method has low dependence on GPS/INS, high positioning accuracy (with the global drift under 1%) and good environmental robustness. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) maps are indispensable for autonomous robot navigation in outdoor environments. Loop closure, which is a key technology in robotic mapping, is essential for creating consistent maps. This paper proposes an efficient method of loop detection for 3D-mapping. The proposed method detects loop constraints by estimating robot poses in revisited places using point-cloud registration. A difficulty in the registration is a large set of 3D points obtained by laser sensors, which requires a long processing time. To reduce the processing time, the proposed method employs a coarse-to-fine approach. Coarse estimation is performed using planes, lines, and balls instead of 3D points, and reduces the hypothesized loop constraints using geometric constraints between the segments. Subsequently, fine estimation is performed using the iterative closest points (ICP) algorithm and 3D points. Another difficulty is the precision of loop detection. To increase the precision, the proposed method employs robustification techniques such as outlier removal in the registration, combination of feature-based and pose-based methods, and robust pose adjustment. Experiments using large-scale datasets show that the proposed method realizes realtime loop detection in a variety of outdoor environments including cities, parks, and forest areas. [ABSTRACT FROM AUTHOR]

Thispaper presents a Neural Network aimed at robust and fast visual loop detection in underwater environments. The proposal is based on an autoencoder architecture, in which the decoder part is being replaced by three fully connected layers. In order to help the proposed network to learn the features that define loop closings, two different global image descriptors to be targeted during training are proposed. Also, a method allowing unsupervised training is presented. The experiments, performed in coastal areas of Mallorca (Spain), show the validity of our proposal and compares it to previously existing methods, based on pre-engineered and learned descriptors. [ABSTRACT FROM AUTHOR]

We present a visual simultaneous localization and mapping, in which a deep neural network is adopted for the loop detection. Its working principles, including the tracking, local mapping, loop detection, and global optimization, are set forth in detail. Its overall performance regarding the loop detection and trajectory estimation is investigated. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is one of the key technologies for coal mine underground operation vehicles to build complex environment maps and positioning and to realize unmanned and autonomous operation. Many domestic and foreign scholars have studied many SLAM algorithms, but the mapping accuracy and real-time performance still need to be further improved. This paper presents a SLAM algorithm integrating scan context and Light weight and Ground-Optimized LiDAR Odometry and Mapping (LeGO-LOAM), LeGO-LOAM-SC. The algorithm uses the global descriptor extracted by scan context for loop detection, adds pose constraints to Georgia Tech Smoothing and Mapping (GTSAM) by Iterative Closest Points (ICP) for graph optimization, and constructs point cloud map and an output estimated pose of the mobile vehicle. The test with KITTI dataset 00 sequence data and the actual test in 2-storey underground parking lots are carried out. The results show that the proposed improved algorithm makes up for the drift of the point cloud map, has a higher mapping accuracy, a better real-time performance, a lower resource occupancy, a higher coincidence between trajectory estimation and real trajectory, smoother loop, and 6% reduction in CPU occupancy, the mean square errors of absolute trajectory error (ATE) and relative pose error (RPE) are reduced by 55.7% and 50.3% respectively; the translation and rotation accuracy are improved by about 5%, and the time consumption is reduced by 2~4%. Accurate map construction and low drift pose estimation can be performed.

Herein, a visual simultaneous localization and mapping (SLAM) is proposed in which both points and lines are extracted as features and a deep neural network is adopted for loop detection. Its working principles, including the representation, extraction, description, and matching of lines, initialization, keyframe selection, optimization of tracking and mapping, and loop detection using a deep neural network, are set forth in detail. The overall trajectory estimation and loop detection performance is investigated using the TUM RGB‐D (indoor) benchmark and KITTI (outdoor) datasets. Compared with the conventional SLAMs, the experimental results of this study indicate that the proposed SLAM is able to improve the accuracy and robustness of trajectory estimation, especially for the scenes with insufficient points. As for loop detection, the deep neural network turns out to be superior to the traditional bag‐of‐words model, because it decreases the accumulated errors of both the estimated trajectory and reconstructed scenes.

In this work, object-oriented integrated algorithms for an efficient flow analysis of the water pipe network are developed. This is achieved by treating the pipe network as a graph data structure with its nodes as the graph's nodes and the pipes as the edges. The algorithm for cycle (real cycle or pseudo-cycle) extraction has been developed using nested breadth-first search that gives ordered cycles. Pseudo-loops are found using the shortest path algorithm between the nodes. Pipes are initialized loop by loop using conservation of mass at nodes. A modified Hardy Cross method is used in the proposed work with third-order convergence. The friction factor is updated for every change in discharges. The pressure calculation has been done by the graph traversal algorithm between the reference nodes and node where the pressure is to be calculated using the energy equation. The pressure at all intermediate nodes is obtained in the course of the traversal. Balanced discharges and nodal pressure in the pipe network are compared with the simultaneous loop flow adjustment method and EPANET software. The proposed work gives more efficient flow analysis than the traditional Newton--Raphson-based techniques for complex networks. [ABSTRACT FROM AUTHOR]

We present two general multi-stage (MS) bit-flipping (BF) decoding algorithms for low-density parity-check (LDPC) codes. Both algorithms consist of soft-decision (SD) and hard-decision BF decoding parts. In comparison with known MS LDPC decoders, our approach is much simpler as all stages share the same BF structure. The only complexity increase is due to the use of an adaptive stage-switching (SS) mechanism which gives near-optimal SS timing. A new design issue we address is that the first-stage algorithm’s parameter has to be re-tuned to achieve the optimal overall performance. The numerical results demonstrate that the proposed decoding methods can significantly improve the error-rate performance of the conventional SD BF decoders. [ABSTRACT FROM AUTHOR]

To achieve the ability of associating continuous-time laser frames is of vital importance but challenging for hand-held or backpack simultaneous localization and mapping (SLAM). In this study, the complex associating and mapping problem is investigated and modeled as a multilayer optimization problem to realize low drift localization and point cloud map reconstruction without the assistance of the GNSS/INS navigation systems. 3D point clouds are aligned among consecutive frames, submaps, and closed-loop frames using the normal distributions transform (NDT) algorithm and the iterative closest point (ICP) algorithm. The ground points are extracted automatically, while the non-ground points are automatically segmented to different point clusters with some noise point clusters omitted before 3D point clouds are aligned. Through the three levels of interframe association, submap matching and closed-loop optimization, the continuous-time laser frames can be accurately associated to guarantee the consistency of 3D point cloud map. Finally, the proposed method was evaluated in different scenarios, the experimental results showed that the proposed method could not only achieve accurate mapping even in the complex scenes, but also successfully handle sparse laser frames well, which is critical for the scanners such as the new Velodyne VLP-16 scanner’s performance.

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper we present a reliable method to verify the existence of loops along the uncertain trajectory of a robot, based on proprioceptive measurements only, within a bounded-error context. The loop closure detection is one of the key points in simultaneous localization and mapping (SLAM) methods, especially in homogeneous environments with difficult scenes recognitions. The proposed approach is generic and could be coupled with conventional SLAM algorithms to reliably reduce their computing burden, thus improving the localization and mapping processes in the most challenging environments such as unexplored underwater extents. To prove that a robot performed a loop whatever the uncertainties in its evolution, we employ the notion of topological degree that originates in the field of differential topology. We show that a verification tool based on the topological degree is an optimal method for proving robot loops. This is demonstrated both on datasets from real missions involving autonomous underwater vehicles and by a mathematical discussion. [ABSTRACT FROM AUTHOR]

Vision-based simultaneous location and mapping(VSLAM)is a popular research topic in robot positioning area. It plays a significant role in robot positioning and scene recognition, task execution, path planning. This paper summarized the application areas and development trends of VSLAM, and analyzed the fundamental principle of VSLAM. On this basis, it surveyed the key technologies and latest research progress of VSLAM from indirect and direct methods, and discussed the comparative advantages and the implementation difficulties of different methods. Finally, it prospected the future development trend and research direction of VSLAM. [ABSTRACT FROM AUTHOR]

Unmanned mining is one of the most effective methods to solve mine safety and low efficiency. However, it is the key to accurate localization and mapping for underground mining environment. A novel graph simultaneous localization and mapping (SLAM) optimization method is proposed, which is based on Generalized Iterative Closest Point (GICP) three-dimensional (3D) point cloud registration between consecutive frames, between consecutive key frames and between loop frames, and is constrained by roadway plane and loop. GICP-based 3D point cloud registration between consecutive frames and consecutive key frames is first combined to optimize laser odometer constraints without other sensors such as inertial measurement unit (IMU). According to the characteristics of the roadway, the innovative extraction of the roadway plane as the node constraint of pose graph SLAM, in addition to automatic removing the noise point cloud to further improve the consistency of the underground roadway map. A lightweight and efficient loop detection and optimization based on rules and GICP is designed. Finally, the proposed method was evaluated in four scenes (such as the underground mine laboratory), and compared with the existing 3D laser SLAM method (such as Lidar Odometry and Mapping (LOAM)). The results show that the algorithm could realize low drift localization and point cloud map construction. This method provides technical support for localization and navigation of underground mining environment.

In order to improve the performance of non-binary low-density parity check codes (LDPC) hard decision decoding algorithm and to reduce the complexity of decoding, a sum of the magnitude for hard decision decoding algorithm based on loop update detection is proposed. This will also ensure the reliability, stability and high transmission rate of 5G mobile communication. The algorithm is based on the hard decision decoding algorithm (HDA) and uses the soft information from the channel to calculate the reliability, while the sum of the variable nodes' (VN) magnitude is excluded for computing the reliability of the parity checks. At the same time, the reliability information of the variable node is considered and the loop update detection algorithm is introduced. The bit corresponding to the error code word is flipped multiple times, before this is searched in the order of most likely error probability to finally find the correct code word. Simulation results show that the performance of one of the improved schemes is better than the weighted symbol flipping (WSF) algorithm under different hexadecimal numbers by about 2.2 dB and 2.35 dB at the bit error rate (BER) of 10-5 over an additive white Gaussian noise (AWGN) channel, respectively. Furthermore, the average number of decoding iterations is significantly reduced. [ABSTRACT FROM AUTHOR]

Learning robot task models with loops helps to increase both the applicability and the compactness of task knowledge. In the framework of Experience-Based Planning Domains (EBPDs), previously formalized by the authors, an approach was developed for learning and exploiting high-level robot task models (the so-called activity schemata) with loops. The paper focuses on the development of: ( i ) a method— Contiguous Non-overlapping Longest Common Subsequence (CNLCS)—based on the Longest Common Prefix (LCP) array for detecting loops of actions in a robot experience; and ( ii ) an abstract planner to instanciate a learned task model with loops for solving particular instances of the same task with varying numbers of objects. Demonstrations of this system in both real and simulated environments prove its potentialities. [ABSTRACT FROM AUTHOR]

In this paper, we modify the gradient descent bit-flipping (GDBF) decoding of low-density parity-check codes based on the syndrome. Firstly, the syndrome weight is utilized to detect the decoding loop, which seriously effects the performance of GDBF decoding. Then the syndrome information is introduced to update the reliability of the flipped bit nodes. Since the modified GDBF, denoted as MGDBF, only uses the syndrome weight and syndrome information, there is small complexity increased. Simulation results indicate that the two modifications bring about significant improvement in error-rate performance. For single MGDBF decoding, its performance is not only better than that of GDBF, but also is better than that of noisy GDBF. For multi MGDBF decoding, it can obtain fast convergence rate and good performance by employing the appropriate adaptive threshold scheme. [ABSTRACT FROM AUTHOR]

In this paper, we present a complete loop detection and correction system developed for data originating from lidar scanners. Regarding detection, we propose a combination of a global point cloud matcher with a novel registration algorithm to determine loop candidates in a highly effective way. The registration method can deal with point clouds that are largely deviating in orientation while improving the efficiency over existing techniques. In addition, we accelerated the computation of the global point cloud matcher by a factor of 2–4, exploiting the GPU to its maximum. Experiments demonstrated that our combined approach more reliably detects loops in lidar data compared to other point cloud matchers as it leads to better precision–recall trade-offs: for nearly 100% recall, we gain up to 7% in precision. Finally, we present a novel loop correction algorithm that leads to an improvement by a factor of 2 on the average and median pose error, while at the same time only requires a handful of seconds to complete.