A robust, resource-efficient, distributed, and minimally parameterized 3D map matching and merging algorithm is proposed. The suggested algorithm utilizes tomographic features from 2D projections of horizontal cross-sections of gravity-aligned local maps, and matches these projection slices at all possible height differences, enabling the estimation of four degrees of freedom in an efficient and parallelizable manner. The advocated algorithm improves state-of-the-art feature extraction and registration pipelines by an order of magnitude in memory use and execution time. Experimental studies are offered to investigate the efficiency of this 3D map merging scheme.
@article{arxiv.2406.19461,
title = {Efficient and Distributed Large-Scale 3D Map Registration using Tomographic Features},
author = {Halil Utku Unlu and Anthony Tzes and Prashanth Krishnamurthy and Farshad Khorrami},
journal= {arXiv preprint arXiv:2406.19461},
year = {2024}
}
Comments
Submitted to Elsevier Journal: Robotics and Autonomous Systems (RAS)