Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras

• URL: http://arxiv.org/abs/2207.06058v1
• Date: Wed, 13 Jul 2022 09:05:35 GMT
• Title: Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras
• Authors: Fangwen Shu, Jiaxuan Wang, Alain Pagani, Didier Stricker
• Abstract summary: This paper demonstrates a visual SLAM system that utilizes point and line cloud for robust camera localization, simultaneously, with an embedded piece-wise planar reconstruction (PPR) module. We address the challenge of reconstructing geometric primitives with scale ambiguity by proposing several run-time optimizations on the reconstructed lines and planes. The results show that our proposed SLAM tightly incorporates the semantic features to boost both tracking as well as backend optimization.
• Score: 13.693353009049773
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper demonstrates a visual SLAM system that utilizes point and line cloud for robust camera localization, simultaneously, with an embedded piece-wise planar reconstruction (PPR) module which in all provides a structural map. To build a scale consistent map in parallel with tracking, such as employing a single camera brings the challenge of reconstructing geometric primitives with scale ambiguity, and further introduces the difficulty in graph optimization of bundle adjustment (BA). We address these problems by proposing several run-time optimizations on the reconstructed lines and planes. The system is then extended with depth and stereo sensors based on the design of the monocular framework. The results show that our proposed SLAM tightly incorporates the semantic features to boost both frontend tracking as well as backend optimization. We evaluate our system exhaustively on various datasets, and open-source our code for the community (https://github.com/PeterFWS/Structure-PLP-SLAM).

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