InTEn-LOAM: Intensity and Temporal Enhanced LiDAR Odometry and Mapping

TL;DR

InTEn-LOAM introduces a robust LiDAR odometry and mapping approach that leverages geometric, intensity, and temporal information, effectively handling dynamic and unstructured environments with low drift and high accuracy.

Contribution

The paper presents a novel intensity-based registration algorithm and a temporal dynamic object removal method, enhancing LiDAR odometry robustness and accuracy in challenging scenarios.

Findings

Achieves comparable or superior accuracy to state-of-the-art methods in normal driving conditions.

Outperforms geometric-based LO in unstructured environments.

Effectively filters dynamic objects using temporal information.

Abstract

Traditional LiDAR odometry (LO) systems mainly leverage geometric information obtained from the traversed surroundings to register laser scans and estimate LiDAR ego-motion, while it may be unreliable in dynamic or unstructured environments. This paper proposes InTEn-LOAM, a low-drift and robust LiDAR odometry and mapping method that fully exploits implicit information of laser sweeps (i.e., geometric, intensity, and temporal characteristics). Scanned points are projected to cylindrical images, which facilitate the efficient and adaptive extraction of various types of features, i.e., ground, beam, facade, and reflector. We propose a novel intensity-based points registration algorithm and incorporate it into the LiDAR odometry, enabling the LO system to jointly estimate the LiDAR ego-motion using both geometric and intensity feature points. To eliminate the interference of dynamic objects, we propose a temporal-based dynamic object removal approach to filter them out before map update. Moreover, the local map is organized and downsampled using a temporal-related voxel grid filter to maintain the similarity between the current scan and the static local map. Extensive experiments are conducted on both simulated and real-world datasets. The results show that the proposed method achieves similar or better accuracy w.r.t the state-of-the-arts in normal driving scenarios and outperforms geometric-based LO in unstructured environments.