Have I been here before? Learning to Close the Loop with LiDAR Data in Graph-Based SLAM

03/11/2021
by   Tim-Lukas Habich, et al.
0

This work presents an extension of graph-based SLAM methods to exploit the potential of 3D laser scans for loop detection. Every high-dimensional point cloud is replaced by a compact global descriptor, whereby a trained detector decides whether a loop exists. Searching for loops is performed locally in a variable space to consider the odometry drift. Since closing a wrong loop has fatal consequences, an extensive verification is performed before acceptance. The proposed algorithm is implemented as an extension of the widely used state-of-the-art library RTAB-Map, and several experiments show the improvement: During SLAM with a mobile service robot in changing indoor and outdoor campus environments, our approach improves RTAB-Map regarding total number of closed loops. Especially in the presence of significant environmental changes, which typically lead to failure, localization becomes possible by our extension. Experiments with a car in traffic (KITTI benchmark) show the general applicability of our approach. These results are comparable to the state-of-the-art LiDAR method LOAM. The developed ROS package is freely available.

READ FULL TEXT

page 1

page 4

page 6

research
06/22/2021

SA-LOAM: Semantic-aided LiDAR SLAM with Loop Closure

LiDAR-based SLAM system is admittedly more accurate and stable than othe...
research
05/24/2021

OverlapNet: Loop Closing for LiDAR-based SLAM

Simultaneous localization and mapping (SLAM) is a fundamental capability...
research
09/27/2021

CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure

Multi-beam LiDAR sensors are increasingly used in robotics, particularly...
research
01/28/2020

Online LiDAR-SLAM for Legged Robots with Robust Registration and Deep-Learned Loop Closure

In this paper, we present a factor-graph LiDAR-SLAM system which incorpo...
research
07/17/2023

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 ident...
research
09/15/2023

Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM

Loop closing and relocalization are crucial techniques to establish reli...
research
07/14/2022

Semi-supervised Vector-Quantization in Visual SLAM using HGCN

In this paper, two semi-supervised appearance based loop closure detecti...

Please sign up or login with your details

Forgot password? Click here to reset