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

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