A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors Article Swipe

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YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1909.07267

Place recognition is a core component of Simultaneous Localization and Mapping (SLAM) algorithms. Particularly in visual SLAM systems, previously-visited places are recognized by measuring the appearance similarity between images representing these locations. However, such approaches are sensitive to visual appearance change and also can be computationally expensive. In this paper, we propose an alternative approach adapting LiDAR descriptors for 3D points obtained from stereo-visual odometry for place recognition. 3D points are potentially more reliable than 2D visual cues (e.g., 2D features) against environmental changes (e.g., variable illumination) and this may benefit visual SLAM systems in long-term deployment scenarios. Stereo-visual odometry generates 3D points with an absolute scale, which enables us to use LiDAR descriptors for place recognition with high computational efficiency. Through extensive evaluations on standard benchmark datasets, we demonstrate the accuracy, efficiency, and robustness of using 3D points for place recognition over 2D methods.

Related Topics

Artificial Intelligence

Simultaneous Localization And Mapping

Odometry

Benchmark (Surveying)

Concepts

Artificial intelligence Visual odometry Lidar Robustness (evolution) Computer vision Computer science Simultaneous localization and mapping Odometry Benchmark (surveying) Stereopsis Pattern recognition (psychology) Robot Mobile robot Remote sensing Geography Cartography Chemistry Gene Biochemistry

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/1909.07267
PDF: https://arxiv.org/pdf/1909.07267
OA Status: green
References: 28
Related Works: 10
OpenAlex ID: https://openalex.org/W3038748551

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DOI: https://doi.org/10.48550/arxiv.1909.07267

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Title: A Fast and Robust Place Recognition Approach for Stereo Visual Odometry Using LiDAR Descriptors

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Type: preprint

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Language: en

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Publication year: 2019

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Publication date: 2019-09-16

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Authors: Jiawei Mo, Junaed Sattar

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Landing page: https://arxiv.org/abs/1909.07267

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PDF URL: https://arxiv.org/pdf/1909.07267

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Concepts: Artificial intelligence, Visual odometry, Lidar, Robustness (evolution), Computer vision, Computer science, Simultaneous localization and mapping, Odometry, Benchmark (surveying), Stereopsis, Pattern recognition (psychology), Robot, Mobile robot, Remote sensing, Geography, Cartography, Chemistry, Gene, Biochemistry

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Cited by: 0

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References (count): 28

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Related works (count): 10

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abstract_inverted_index.recognized	21
abstract_inverted_index.robustness	134
abstract_inverted_index.scenarios.	97
abstract_inverted_index.similarity	26
abstract_inverted_index.algorithms.	12
abstract_inverted_index.alternative	53
abstract_inverted_index.demonstrate	129
abstract_inverted_index.descriptors	57, 113
abstract_inverted_index.efficiency,	132
abstract_inverted_index.efficiency.	120
abstract_inverted_index.evaluations	123
abstract_inverted_index.potentially	71
abstract_inverted_index.recognition	1, 116, 141
abstract_inverted_index.Localization	8
abstract_inverted_index.Particularly	13
abstract_inverted_index.Simultaneous	7
abstract_inverted_index.recognition.	67
abstract_inverted_index.representing	29
abstract_inverted_index.Stereo-visual	98
abstract_inverted_index.computational	119
abstract_inverted_index.environmental	82
abstract_inverted_index.illumination)	86
abstract_inverted_index.stereo-visual	63
abstract_inverted_index.computationally	45
abstract_inverted_index.previously-visited	18
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	2
citation_normalized_percentile