scholarly journals c-M2DP: A Fast Point Cloud Descriptor with Color Information to Perform Loop Closure Detection

2019 ◽  
Author(s):  
Leonardo Perdomo ◽  
Diego Pittol ◽  
Mathias Mantelli ◽  
Renan Maffei ◽  
Mariana Kolberg ◽  
...  
Keyword(s):  
Point Cloud ◽  
Color Information ◽  
Loop Closure ◽  
Loop Closure Detection

scholarly journals A Novel Loop Closure Detection Approach Using Simplified Structure for Low-Cost LiDAR

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2299
Author(s):  
Qin Ye ◽  
Pengcheng Shi ◽  
Kunyuan Xu ◽  
Popo Gui ◽  
Shaoming Zhang
Keyword(s):  
Point Cloud ◽  
Detection Efficiency ◽  
Low Cost ◽  
Evaluation Model ◽  
Level Structure ◽  
Geometric Constraints ◽  
Global Localization ◽  
Point Cloud Registration ◽  
Loop Closure ◽  
Loop Closure Detection

Reducing the cumulative error is a crucial task in simultaneous localization and mapping (SLAM). Usually, Loop Closure Detection (LCD) is exploited to accomplish this work for SLAM and robot navigation. With a fast and accurate loop detection, it can significantly improve global localization stability and reduce mapping errors. However, the LCD task based on point cloud still has some problems, such as over-reliance on high-resolution sensors, and poor detection efficiency and accuracy. Therefore, in this paper, we propose a novel and fast global LCD method using a low-cost 16 beam Lidar based on “Simplified Structure”. Firstly, we extract the “Simplified Structure” from the indoor point cloud, classify them into two levels, and manage the “Simplified Structure” hierarchically according to its structure salience. The “Simplified Structure” has simple feature geometry and can be exploited to capture the indoor stable structures. Secondly, we analyze the point cloud registration suitability with a pre-match, and present a hierarchical matching strategy with multiple geometric constraints in Euclidean Space to match two scans. Finally, we construct a multi-state loop evaluation model for a multi-level structure to determine whether the two candidate scans are a loop. In fact, our method also provides a transformation for point cloud registration with “Simplified Structure” when a loop is detected successfully. Experiments are carried out on three types of indoor environment. A 16 beam Lidar is used to collect data. The experimental results demonstrate that our method can detect global loop closures efficiently and accurately. The average global LCD precision, accuracy and negative are approximately 0.90, 0.96, and 0.97, respectively.

scholarly journals LIO-CSI: LiDAR inertial odometry with loop closure combined with semantic information

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261053
Author(s):  
Gang Wang ◽  
Saihang Gao ◽  
Han Ding ◽  
Hao Zhang ◽  
Hongmin Cai
Keyword(s):  
Point Cloud ◽  
Semantic Information ◽  
Feature Matching ◽  
Autonomous Driving ◽  
Geometric Feature ◽  
Semantic Features ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Front End ◽  
Dynamic Objects

Accurate and reliable state estimation and mapping are the foundation of most autonomous driving systems. In recent years, researchers have focused on pose estimation through geometric feature matching. However, most of the works in the literature assume a static scenario. Moreover, a registration based on a geometric feature is vulnerable to the interference of a dynamic object, resulting in a decline of accuracy. With the development of a deep semantic segmentation network, we can conveniently obtain the semantic information from the point cloud in addition to geometric information. Semantic features can be used as an accessory to geometric features that can improve the performance of odometry and loop closure detection. In a more realistic environment, semantic information can filter out dynamic objects in the data, such as pedestrians and vehicles, which lead to information redundancy in generated map and map-based localization failure. In this paper, we propose a method called LiDAR inertial odometry (LIO) with loop closure combined with semantic information (LIO-CSI), which integrates semantic information to facilitate the front-end process as well as loop closure detection. First, we made a local optimization on the semantic labels provided by the Sparse Point-Voxel Neural Architecture Search (SPVNAS) network. The optimized semantic information is combined into the front-end process of tightly-coupled light detection and ranging (LiDAR) inertial odometry via smoothing and mapping (LIO-SAM), which allows us to filter dynamic objects and improve the accuracy of the point cloud registration. Then, we proposed a semantic assisted scan-context method to improve the accuracy and robustness of loop closure detection. The experiments were conducted on an extensively used dataset KITTI and a self-collected dataset on the Jilin University (JLU) campus. The experimental results demonstrate that our method is better than the purely geometric method, especially in dynamic scenarios, and it has a good generalization ability.

scholarly journals Visual SLAM Framework Based on Segmentation with the Improvement of Loop Closure Detection in Dynamic Environments

2021 ◽  
Vol 33 (6) ◽  
pp. 1385-1397
Author(s):  
Leyuan Sun ◽  
Rohan P. Singh ◽  
Fumio Kanehiro ◽  
◽  
◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Dynamic Environments ◽  
Camera Tracking ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Multiple Datasets ◽  
Localization And Mapping ◽  
Object Based ◽  
Precision Recall Curve

Most simultaneous localization and mapping (SLAM) systems assume that SLAM is conducted in a static environment. When SLAM is used in dynamic environments, the accuracy of each part of the SLAM system is adversely affected. We term this problem as dynamic SLAM. In this study, we propose solutions for three main problems in dynamic SLAM: camera tracking, three-dimensional map reconstruction, and loop closure detection. We propose to employ geometry-based method, deep learning-based method, and the combination of them for object segmentation. Using the information from segmentation to generate the mask, we filter the keypoints that lead to errors in visual odometry and features extracted by the CNN from dynamic areas to improve the performance of loop closure detection. Then, we validate our proposed loop closure detection method using the precision-recall curve and also confirm the framework’s performance using multiple datasets. The absolute trajectory error and relative pose error are used as metrics to evaluate the accuracy of the proposed SLAM framework in comparison with state-of-the-art methods. The findings of this study can potentially improve the robustness of SLAM technology in situations where mobile robots work together with humans, while the object-based point cloud byproduct has potential for other robotics tasks.

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