NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM

Loop-closure detection, also known as place recognition, aiming to identify previously visited locations, is an essential component of a SLAM system. Existing research on lidar-based loop closure heavily relies on dense point cloud and 360 FOV lidars. This paper proposes an out-of-the-box NDT (Normal Distribution Transform) based global descriptor, NDT-Map-Code, designed for both on-road driving and underground valet parking scenarios. NDT-Map-Code can be directly extracted from the NDT map without the need for a dense point cloud, resulting in excellent scalability and low maintenance cost. The NDT representation is leveraged to identify representative patterns, which are further encoded according to their spatial location (bearing, range, and height). Experimental results on the NIO underground parking lot dataset and the KITTI dataset demonstrate that our method achieves significantly better performance compared to the state-of-the-art.

Authors

Institutions

Publication Details

Published
2024-10-14
DOI
https://doi.org/10.1109/iros58592.2024.10801384
Citations
6
Primary Topic
Robotics and Sensor-Based Localization
Type
article
Field-Weighted Citation Impact
7.79
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM

Chunyun Fu, Xinhui Bai, Lizhou Liao, Zhenxing You et al.
6 citations
Robotics and Sensor-Based Localization
7.79
article

NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM

Chunyun Fu, Xinhui Bai, Lizhou Liao, Zhenxing You, Weixin Yan, Li Sun, Hongyuan Yuan
article en
6 citations

Abstract

Loop-closure detection, also known as place recognition, aiming to identify previously visited locations, is an essential component of a SLAM system. Existing research on lidar-based loop closure heavily relies on dense point cloud and 360 FOV lidars. This paper proposes an out-of-the-box NDT (Normal Distribution Transform) based global descriptor, NDT-Map-Code, designed for both on-road driving and underground valet parking scenarios. NDT-Map-Code can be directly extracted from the NDT map without the need for a dense point cloud, resulting in excellent scalability and low maintenance cost. The NDT representation is leveraged to identify representative patterns, which are further encoded according to their spatial location (bearing, range, and height). Experimental results on the NIO underground parking lot dataset and the KITTI dataset demonstrate that our method achieves significantly better performance compared to the state-of-the-art.

Chongqing University (CN)
Openalex Percentile: Top 3%
Robotics and Sensor-Based Localization
7.79
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.