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Comparative Evaluation of RGB-D SLAM Methods for Humanoid Robot Localization and Mapping

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Amirhosein Vedadi, Aghil Yousefi-Koma, Parsa Yazdankhah, Amin Mozayyan

Robotics · Submitted 5 Jan 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In assessing localization accuracy, ORB-SLAM3 outperformed the others with an ATE of 0.1073, followed by RTAB-Map at 0.1641 and OpenVSLAM at 0.1847.

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arXiv ID2401.02816 (version 1)
Pith IDSIOVRRBU, Pith's permanent record of this paper and its review
CitationAghil Yousefi-Koma, Amin Mozayyan, Amirhosein Vedadi, Parsa Yazdankhah. "Comparative Evaluation of RGB-D SLAM Methods for Humanoid Robot Localization and Mapping." arXiv:2401.02816 (2024). https://pith.science/paper/SIOVRRBU
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,750 input tokens.

What the abstract itself states, as probabilities: claims first 0.02 · states limitation 0.80 · human participants 0.04 · controlled comparison 0.16 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.84 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this paper, we conducted a comparative evaluation of three RGB-D SLAM (Simultaneous Localization and Mapping) algorithms: RTAB-Map, ORB-SLAM3, and OpenVSLAM for SURENA-V humanoid robot localization and mapping. Our test involves the robot to follow a full circular pattern, with an Intel RealSense D435 RGB-D camera installed on its head. In assessing localization accuracy, ORB-SLAM3 outperformed the others with an ATE of 0.1073, followed by RTAB-Map at 0.1641 and OpenVSLAM at 0.1847. However, it should be noted that both ORB-SLAM3 and OpenVSLAM faced challenges in maintaining accurate odometry when the robot encountered a wall with limited feature points. Nevertheless, OpenVSLAM demonstrated the ability to detect loop closures and successfully relocalize itself within the map when the robot approached its initial location. The investigation also extended to mapping capabilities, where RTAB-Map excelled by offering diverse mapping outputs, including dense, OctoMap, and occupancy grid maps. In contrast, both ORB-SLAM3 and OpenVSLAM provided only sparse maps.

background contribution method result interpretation main claim

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#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper, we conducted a comparative evaluation of three RGB-D SLAM (Simultaneous Localization and Mapping) algorithms: RTAB-Map, ORB-SLAM3, and OpenVSLAM for SURENA-V humanoid robot localization and mapping. contribution 1.00 0.95 0.05 asserted 0.98
s1 Our test involves the robot to follow a full circular pattern, with an Intel RealSense D435 RGB-D camera installed on its head. method 1.00 0.95 0.02 asserted 0.98
s2 In assessing localization accuracy, ORB-SLAM3 outperformed the others with an ATE of 0.1073, followed by RTAB-Map at 0.1641 and OpenVSLAM at 0.1847. result 1.00 0.96 0.35 asserted 1.00
s3 However, it should be noted that both ORB-SLAM3 and OpenVSLAM faced challenges in maintaining accurate odometry when the robot encountered a wall with limited feature points. limitation 0.59 0.92 0.06 asserted 0.98
s4 Nevertheless, OpenVSLAM demonstrated the ability to detect loop closures and successfully relocalize itself within the map when the robot approached its initial location. result 1.00 0.94 0.06 asserted 1.00
s5 The investigation also extended to mapping capabilities, where RTAB-Map excelled by offering diverse mapping outputs, including dense, OctoMap, and occupancy grid maps. result 1.00 0.93 0.10 asserted 1.00
s6 In contrast, both ORB-SLAM3 and OpenVSLAM provided only sparse maps. result 1.00 0.91 0.08 asserted 1.00

What kind of paper the classifier reports this is: empirical study 1.00 · evidence from observation or experiment 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In this paper, we conducted a comparative evaluation of three RGB-D SLAM (Simultaneous Localization and Mapping) algorithms: RTAB-Map, ORB-SLAM3, and OpenVSLAM for SURENA-V humanoid robot localization and mapping. Our test involves the robot to follow a full circular pattern, with an Intel RealSense D435 RGB-D camera installed on its head. In assessing localization accuracy, ORB-SLAM3 outperformed the others with an ATE of 0.1073, followed by RTAB-Map at 0.1641 and OpenVSLAM at 0.1847. However, it should be noted that both ORB-SLAM3 and OpenVSLAM faced challenges in maintaining accurate odometry when the robot encountered a wall with limited feature points. Nevertheless, OpenVSLAM demonstrated the ability to detect loop closures and successfully relocalize itself within the map when the robot approached its initial location. The investigation also extended to mapping capabilities, where RTAB-Map excelled by offering diverse mapping outputs, including dense, OctoMap, and occupancy grid maps. In contrast, both ORB-SLAM3 and OpenVSLAM provided only sparse maps.

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