Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency Article Swipe

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Xingwu Ji , Peilin Liu , Haochen Niu , Xiang Chen , Rendong Ying , Fei Wen ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2304.05146

Visual simultaneous localization and mapping (SLAM) systems face challenges in detecting loop closure under the circumstance of large viewpoint changes. In this paper, we present an object-based loop closure detection method based on the spatial layout and semanic consistency of the 3D scene graph. Firstly, we propose an object-level data association approach based on the semantic information from semantic labels, intersection over union (IoU), object color, and object embedding. Subsequently, multi-view bundle adjustment with the associated objects is utilized to jointly optimize the poses of objects and cameras. We represent the refined objects as a 3D spatial graph with semantics and topology. Then, we propose a graph matching approach to select correspondence objects based on the structure layout and semantic property similarity of vertices' neighbors. Finally, we jointly optimize camera trajectories and object poses in an object-level pose graph optimization, which results in a globally consistent map. Experimental results demonstrate that our proposed data association approach can construct more accurate 3D semantic maps, and our loop closure method is more robust than point-based and object-based methods in circumstances with large viewpoint changes.

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Computer science Artificial intelligence Consistency (knowledge bases) Computer vision Graph Object (grammar) Matching (statistics) Simultaneous localization and mapping Object-based spatial database Intersection (aeronautics) Pattern recognition (psychology) Theoretical computer science Data mining Mathematics Spatial analysis Spatial database Statistics Mobile robot Aerospace engineering Robot Engineering

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2304.05146
PDF: https://arxiv.org/pdf/2304.05146
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Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2304.05146

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Title: Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency

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Type: preprint

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Language: en

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Publication year: 2023

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Publication date: 2023-04-11

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Authors: Xingwu Ji, Peilin Liu, Haochen Niu, Xiang Chen, Rendong Ying, Fei Wen

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Landing page: https://arxiv.org/abs/2304.05146

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PDF URL: https://arxiv.org/pdf/2304.05146

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Concepts: Computer science, Artificial intelligence, Consistency (knowledge bases), Computer vision, Graph, Object (grammar), Matching (statistics), Simultaneous localization and mapping, Object-based spatial database, Intersection (aeronautics), Pattern recognition (psychology), Theoretical computer science, Data mining, Mathematics, Spatial analysis, Spatial database, Statistics, Mobile robot, Aerospace engineering, Robot, Engineering

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Cited by: 1

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abstract_inverted_index.associated	75
abstract_inverted_index.challenges	8
abstract_inverted_index.consistent	145
abstract_inverted_index.embedding.	68
abstract_inverted_index.multi-view	70
abstract_inverted_index.neighbors.	124
abstract_inverted_index.similarity	121
abstract_inverted_index.association	50, 154
abstract_inverted_index.consistency	38
abstract_inverted_index.demonstrate	149
abstract_inverted_index.information	56
abstract_inverted_index.point-based	172
abstract_inverted_index.Experimental	147
abstract_inverted_index.circumstance	15
abstract_inverted_index.intersection	60
abstract_inverted_index.localization	2
abstract_inverted_index.object-based	26, 174
abstract_inverted_index.object-level	48, 136
abstract_inverted_index.simultaneous	1
abstract_inverted_index.trajectories	130
abstract_inverted_index.Subsequently,	69
abstract_inverted_index.circumstances	177
abstract_inverted_index.optimization,	139
abstract_inverted_index.correspondence	111
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	6
citation_normalized_percentile