Real-Time Simultaneous Localization and Mapping with LiDAR intensity

TL;DR

This paper introduces a real-time LiDAR intensity image-based SLAM method that overcomes geometry degeneracy issues in unstructured environments by leveraging intensity features for odometry, loop closure, and optimization.

Contribution

It presents a novel approach combining intensity image features with LiDAR data for robust real-time SLAM in challenging environments, addressing limitations of geometric feature reliance.

Findings

Runs in real time with high accuracy

Effective in low-texture and unstructured environments

Robust to illumination changes

Abstract

We propose a novel real-time LiDAR intensity image-based simultaneous localization and mapping method , which addresses the geometry degeneracy problem in unstructured environments. Traditional LiDAR-based front-end odometry mostly relies on geometric features such as points, lines and planes. A lack of these features in the environment can lead to the failure of the entire odometry system. To avoid this problem, we extract feature points from the LiDAR-generated point cloud that match features identified in LiDAR intensity images. We then use the extracted feature points to perform scan registration and estimate the robot ego-movement. For the back-end, we jointly optimize the distance between the corresponding feature points, and the point to plane distance for planes identified in the map. In addition, we use the features extracted from intensity images to detect loop closure candidates from previous scans and perform pose graph optimization. Our experiments show that our method can run in real time with high accuracy and works well with illumination changes, low-texture, and unstructured environments.