Related papers: Leveraging Planar Regularities for Point Line Visual-Inertial Odometry

Leveraging Planar Regularities for Point Line Visual-Inertial Odometry

URL: http://arxiv.org/abs/2004.11969v2
Date: Thu, 14 Jan 2021 07:06:17 GMT
Title: Leveraging Planar Regularities for Point Line Visual-Inertial Odometry
Authors: Xin Li, Yijia He, Jinlong Lin, Xiao Liu
Abstract summary: With monocular Visual-Inertial Odometry (VIO) system, 3D point cloud and camera motion can be estimated simultaneously. We propose PLP-VIO, which exploits point features and line features as well as plane regularities. The effectiveness of the proposed method is verified on both synthetic data and public datasets.
Score: 13.51108336267342
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: With monocular Visual-Inertial Odometry (VIO) system, 3D point cloud and camera motion can be estimated simultaneously. Because pure sparse 3D points provide a structureless representation of the environment, generating 3D mesh from sparse points can further model the environment topology and produce dense mapping. To improve the accuracy of 3D mesh generation and localization, we propose a tightly-coupled monocular VIO system, PLP-VIO, which exploits point features and line features as well as plane regularities. The co-planarity constraints are used to leverage additional structure information for the more accurate estimation of 3D points and spatial lines in state estimator. To detect plane and 3D mesh robustly, we combine both the line features with point features in the detection method. The effectiveness of the proposed method is verified on both synthetic data and public datasets and is compared with other state-of-the-art algorithms.

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