3D LiDAR-Based Global Localization Using Siamese Neural Network

Yin, Huan; Wang, Yue; Ding, Xiaqing; Tang, Li; Huang, Shoudong; Xiong, Rong

doi:10.1109/tits.2019.2905046

Cited by 79 publications

(41 citation statements)

References 34 publications

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“…These global descriptors are based on the structure of the range distribution and efficient for place recognition. Similarly, some learning-based methods first generated the representations according to the statistical properties, then fed them into the classifiers (Granström et al, 2011) or CNN (Yin et al, 2019;Chen et al, 2020). In addition, some researchers proposed to learn the point features in an end-to-end manner recently (Uy and Lee, 2018;Liu et al, 2019), while these methods bring more complexity for network training and recognition inference.…”

Section: Lidar-based Place Recognitionmentioning

confidence: 99%

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

Yin

Wang

et al. 2021

Front. Robot. AI

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Place recognition is critical for both offline mapping and online localization. However, current single-sensor based place recognition still remains challenging in adverse conditions. In this paper, a heterogeneous measurement based framework is proposed for long-term place recognition, which retrieves the query radar scans from the existing lidar (Light Detection and Ranging) maps. To achieve this, a deep neural network is built with joint training in the learning stage, and then in the testing stage, shared embeddings of radar and lidar are extracted for heterogeneous place recognition. To validate the effectiveness of the proposed method, we conducted tests and generalization experiments on the multi-session public datasets and compared them to other competitive methods. The experimental results indicate that our model is able to perform multiple place recognitions: lidar-to-lidar (L2L), radar-to-radar (R2R), and radar-to-lidar (R2L), while the learned model is trained only once. We also release the source code publicly: https://github.com/ZJUYH/radar-to-lidar-place-recognition.

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Section: Lidar-based Place Recognitionmentioning

confidence: 99%

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

Yin

Wang

et al. 2021

Front. Robot. AI

Self Cite

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“…Step 1: Firstly, traversal flag of the grid visit F is initialized to 0. Then, equation 3, (4) and 5 i  is the variance of points in grid i.…”

Section: A Double-layer Region Growingmentioning

confidence: 99%

“…Due to the fact that the camera is greatly affected by light and cannot be used all day long, this study adopts LIDAR characterized by high measurement accuracy and strong anti-interference ability [1]. The purpose of obstacle detection based on LIDAR is to distinguish the point clouds of different obstacles from raw point clouds, and provide necessary basis for the following work such as obstacle classification [2], tracking [3] and LIDAR localization [4][5].…”

Section: Introductionmentioning

confidence: 99%

A Fast Obstacle Detection Method by Fusion of Double-Layer Region Growing Algorithm and Grid-SECOND Detector

Qiu

Zhang

et al. 2021

IEEE Access

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“…In turn, this also involves increased computational requirements to perform the matching. Some approaches have been proposed to mitigate the data processing burden with strategies that include projecting the LiDAR scan to a ground plane [5] or using a siamese network able to learn dimension-reduced representations [6].…”

Section: Related Workmentioning

confidence: 99%

Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry

et al. 2020

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This paper presents a study of how the performance of LiDAR odometry is affected by the preprocessing of the point cloud through the use of 3D semantic segmentation. The study analyzed the estimated trajectories when the semantic information is exploited to filter the original raw data. Different filtering configurations were tested: raw (original point cloud), dynamic (dynamic obstacles are removed from the point cloud), dynamic vehicles (vehicles are removed), far (distant points are removed), ground (the points belonging to the ground are removed) and structure (only structures and objects are kept in the point cloud). The experiments were performed using the KITTI and SemanticKITTI datasets, which feature different scenarios that allowed identifying the implications and relevance of each element of the environment in LiDAR odometry algorithms. The conclusions obtained from this work are of special relevance for improving the efficiency of LiDAR odometry algorithms in all kinds of scenarios.

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3D LiDAR-Based Global Localization Using Siamese Neural Network

Cited by 79 publications

References 34 publications

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning

A Fast Obstacle Detection Method by Fusion of Double-Layer Region Growing Algorithm and Grid-SECOND Detector

Study of the Effect of Exploiting 3D Semantic Segmentation in LiDAR Odometry

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