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“π-LSAM: LiDAR Smoothing and Mapping With Planes” by L. Zhou, S. Wang, and M. Kaess. In Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA, (Xi'an, China), May 2021, pp. 5751-5757.
This paper introduces a real-time dense planar LiDAR SLAM system, named π-LSAM, for the indoor environment. The widely used LiDAR odometry and mapping (LOAM) framework [1] does not include bundle adjustment (BA) and generates a low fidelity tracking pose. This paper seeks to overcome these drawbacks for the indoor environment. Specifically, we use the plane as the landmark, and introduce plane adjustment (PA) as our back-end to jointly optimize planes and keyframe poses. We present the Ï€-factor to significantly reduce the computational complexity of PA. In addition, we introduce an efficient loop detection algorithm based on the RANSAC framework using planes. In the front-end, our algorithm performs global registration in real time. To achieve this performance, we maintain the local-to-global point-to-plane correspondences scan by scan, so that we only need a small local KD-tree to establish the data association between a LiDAR scan and the global planes, rather than a large global KD-tree used in previous works. With this local-to-global data association, our algorithm directly identifies planes in a LiDAR scan, and yields an accurate and globally consistent pose. Experimental results show that our algorithm significantly outperforms the state-of-the-art LOAM variant, LeGO-LOAM [2], and our algorithm achieves real time.
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BibTeX entry:
@inproceedings{Zhou21icra,
author = {L. Zhou and S. Wang and M. Kaess},
title = {{\it \pi}-{LSAM}: {LiDAR} Smoothing and Mapping With Planes},
booktitle = {Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA},
pages = {5751-5757},
address = {Xi'an, China},
month = may,
year = {2021}
}