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JRM Vol.35 No.6 pp. 1622-1628
doi: 10.20965/jrm.2023.p1622
(2023)

Paper:

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Improved Visual Robot Place Recognition of Scan-Context Descriptors by Combining with CNN and SVM

Minying Ye ORCID Icon and Kanji Tanaka ORCID Icon

Human and Artificial Intelligent System Course, Graduate School of Engineering, University of Fukui
3-9-1 Bunkyo, Fukui, Fukui 910-8507, Japan

Received:
April 18, 2023
Accepted:
September 11, 2023
Published:
December 20, 2023
Creative Commons License
Keywords:
visual place recognition, 3D point clouds, classification CNN, SVM
Abstract

Visual place recognition from a 3D laser LiDAR is one of the most active research areas in robotics. Especially, learning and recognition of scene descriptors, such as scan context descriptors that map 3D point clouds to 2D point clouds, is one of the promising research directions. Although the scan-context descriptor has a sufficiently high recognition performance, it is still expensive image data and cannot be handled with low-capacity non-deep models. In this paper, we explore the task of compressing the scan context descriptor model while maintaining its recognition performance. To this end, the proposed approach slightly modifies the off-the-shelf classifier model of convolutional neural networks (CNN) from its basis, by replacing the SoftMax part with a support vector machine (SVM). Experiments with publicly available NCLT dataset validate the effectiveness of the proposed approach.

The CNN_SVM place recognition system

The CNN_SVM place recognition system

Cite this article as:
M. Ye and K. Tanaka, “Improved Visual Robot Place Recognition of Scan-Context Descriptors by Combining with CNN and SVM,” J. Robot. Mechatron., Vol.35 No.6, pp. 1622-1628, 2023.
Data files:
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