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 Dynamic-DSO: Direct Sparse Odometry Using Objects Semantic Information for Dynamic Environments

2020 ◽  
Vol 10 (4) ◽  
pp. 1467
Author(s):  
Chao Sheng ◽  
Shuguo Pan ◽  
Wang Gao ◽  
Yong Tan ◽  
Tao Zhao
Keyword(s):  
Semantic Information ◽  
Direct Method ◽  
Visual Odometry ◽  
Dynamic Environments ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Camera Pose ◽  
Photometric Error ◽  
Tracking Model ◽  
Dynamic Objects

Traditional Simultaneous Localization and Mapping (SLAM) (with loop closure detection), or Visual Odometry (VO) (without loop closure detection), are based on the static environment assumption. When working in dynamic environments, they perform poorly whether using direct methods or indirect methods (feature points methods). In this paper, Dynamic-DSO which is a semantic monocular direct visual odometry based on DSO (Direct Sparse Odometry) is proposed. The proposed system is completely implemented with the direct method, which is different from the most current dynamic systems combining the indirect method with deep learning. Firstly, convolutional neural networks (CNNs) are applied to the original RGB image to generate the pixel-wise semantic information of dynamic objects. Then, based on the semantic information of the dynamic objects, dynamic candidate points are filtered out in keyframes candidate points extraction; only static candidate points are reserved in the tracking and optimization module, to achieve accurate camera pose estimation in dynamic environments. The photometric error calculated by the projection points in dynamic region of subsequent frames are removed from the whole photometric error in pyramid motion tracking model. Finally, the sliding window optimization which neglects the photometric error calculated in the dynamic region of each keyframe is applied to obtain the precise camera pose. Experiments on the public TUM dynamic dataset and the modified Euroc dataset show that the positioning accuracy and robustness of the proposed Dynamic-DSO is significantly higher than the state-of-the-art direct method in dynamic environments, and the semi-dense cloud map constructed by Dynamic-DSO is clearer and more detailed.

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 Point Cloud Semantic Segmentation with Cross-Correction Features

2022 ◽  
Author(s):  
Yuehua Zhao ◽  
Ma Jie ◽  
Chong Nannan ◽  
Wen Junjie
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Large Scale ◽  
Spatial Information ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Basic Unit ◽  
Semantic Features ◽  
Point Cloud Segmentation ◽  
Scale Point

Abstract Real time large scale point cloud segmentation is an important but challenging task for practical application like autonomous driving. Existing real time methods have achieved acceptance performance by aggregating local information. However, most of them only exploit local spatial information or local semantic information dependently, few considering the complementarity of both. In this paper, we propose a model named Spatial-Semantic Incorporation Network (SSI-Net) for real time large scale point cloud segmentation. A Spatial-Semantic Cross-correction (SSC) module is introduced in SSI-Net as a basic unit. High quality contextual features can be learned through SSC by correct and update semantic features using spatial cues, and vice verse. Adopting the plug-and-play SSC module, we design SSI-Net as an encoder-decoder architecture. To ensure efficiency, it also adopts a random sample based hierarchical network structure. Extensive experiments on several prevalent datasets demonstrate that our method can achieve state-of-the-art performance.

scholarly journals Robust Loop Closure Detection Integrating Visual–Spatial–Semantic Information via Topological Graphs and CNN Features

2020 ◽  
Vol 12 (23) ◽  
pp. 3890
Author(s):  
Yuwei Wang ◽  
Yuanying Qiu ◽  
Peitao Cheng ◽  
Xuechao Duan
Keyword(s):  
Semantic Information ◽  
Graph Matching ◽  
Spatial Relationships ◽  
Dynamic Scenes ◽  
Topological Graphs ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Visual Spatial ◽  
Localization And Mapping ◽  
The Impact

Loop closure detection is a key module for visual simultaneous localization and mapping (SLAM). Most previous methods for this module have not made full use of the information provided by images, i.e., they have only used the visual appearance or have only considered the spatial relationships of landmarks; the visual, spatial and semantic information have not been fully integrated. In this paper, a robust loop closure detection approach integrating visual–spatial–semantic information is proposed by employing topological graphs and convolutional neural network (CNN) features. Firstly, to reduce mismatches under different viewpoints, semantic topological graphs are introduced to encode the spatial relationships of landmarks, and random walk descriptors are employed to characterize the topological graphs for graph matching. Secondly, dynamic landmarks are eliminated by using semantic information, and distinctive landmarks are selected for loop closure detection, thus alleviating the impact of dynamic scenes. Finally, to ease the effect of appearance changes, the appearance-invariant descriptor of the landmark region is extracted by a pre-trained CNN without the specially designed manual features. The proposed approach weakens the influence of viewpoint changes and dynamic scenes, and extensive experiments conducted on open datasets and a mobile robot demonstrated that the proposed method has more satisfactory performance compared to state-of-the-art methods.

Loop Closure Detection for Visual SLAM Fusing Semantic Information

2019 ◽  
Author(s):  
Mingyue Hu ◽  
Sheng Li ◽  
Jingyuan Wu ◽  
Jiawei Guo ◽  
Haiyu Li ◽  
...  
Keyword(s):  
Semantic Information ◽  
Visual Slam ◽  
Loop Closure ◽  
Loop Closure Detection
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