Intensity-SLAM: Intensity Assisted Localization and Mapping for Large Scale Environment

TL;DR

This paper introduces a novel SLAM framework that integrates intensity features from LiDAR data with geometric information, improving localization and mapping accuracy in large-scale environments for autonomous robots.

Contribution

It presents a full SLAM system combining intensity and geometric features, enhancing robustness and accuracy over traditional geometric-only methods.

Findings

Outperforms existing geometric-only LiDAR SLAM methods

Effective in both outdoor autonomous driving and indoor warehouse environments

Leverages both front-end odometry and back-end optimization with intensity features

Abstract

Simultaneous Localization And Mapping (SLAM) is a task to estimate the robot location and to reconstruct the environment based on observation from sensors such as LIght Detection And Ranging (LiDAR) and camera. It is widely used in robotic applications such as autonomous driving and drone delivery. Traditional LiDAR-based SLAM algorithms mainly leverage the geometric features from the scene context, while the intensity information from LiDAR is ignored. Some recent deep-learning-based SLAM algorithms consider intensity features and train the pose estimation network in an end-to-end manner. However, they require significant data collection effort and their generalizability to environments other than the trained one remains unclear. In this paper we introduce intensity features to a SLAM system. And we propose a novel full SLAM framework that leverages both geometry and intensity features. The proposed SLAM involves both intensity-based front-end odometry estimation and intensity-based back-end optimization. Thorough experiments are performed including both outdoor autonomous driving and indoor warehouse robot manipulation. The results show that the proposed method outperforms existing geometric-only LiDAR SLAM methods.