FAST-LIO, Then Bayesian ICP, Then GTSFM
Jerred Chen, Xiangcheng Hu, Shicong Ma, Jianhao Jiao, Ming Liu, and, Frank Dellaert
TL;DR
This paper presents a two-stage system for the Hilti Challenge 2022, combining FAST-LIO2 with Bayesian ICP PoseSLAM and a GTSFM structure from motion pipeline with GTSAM backend optimization.
Contribution
It introduces a novel combination of FAST-LIO2, Bayesian ICP, and GTSFM for improved SLAM and structure from motion performance.
Findings
Effective integration of FAST-LIO2 and Bayesian ICP for pose estimation
GTSFM pipeline with GTSAM enhances 3D reconstruction accuracy
System achieves competitive results in the Hilti Challenge 2022
Abstract
For the Hilti Challenge 2022, we created two systems, one building upon the other. The first system is FL2BIPS which utilizes the iEKF algorithm FAST-LIO2 and Bayesian ICP PoseSLAM, whereas the second system is GTSFM, a structure from motion pipeline with factor graph backend optimization powered by GTSAM
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Taxonomy
TopicsArtificial Intelligence in Games · Handwritten Text Recognition Techniques · Natural Language Processing Techniques