Efficient Learning-Based Robotic Navigation Using Feature-Based RGB-D Pose Estimation and Topological Maps.

IF 2.1 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Entropy Pub Date : 2025-06-15 DOI:10.3390/e27060641
Eder A Rodríguez-Martínez, Jesús Elías Miranda-Vega, Farouk Achakir, Oleg Sergiyenko, Julio C Rodríguez-Quiñonez, Daniel Hernández Balbuena, Wendy Flores-Fuentes
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引用次数: 0

Abstract

Robust indoor robot navigation typically demands either costly sensors or extensive training data. We propose a cost-effective RGB-D navigation pipeline that couples feature-based relative pose estimation with a lightweight multi-layer-perceptron (MLP) policy. RGB-D keyframes extracted from human-driven traversals form nodes of a topological map; edges are added when visual similarity and geometric-kinematic constraints are jointly satisfied. During autonomy, LightGlue features and SVD give six-DoF relative pose to the active keyframe, and the MLP predicts one of four discrete actions. Low visual similarity or detected obstacles trigger graph editing and Dijkstra replanning in real time. Across eight tasks in four Habitat-Sim environments, the agent covered 190.44 m, replanning when required, and consistently stopped within 0.1 m of the goal while running on commodity hardware. An information-theoretic analysis over the Multi-Illumination dataset shows that LightGlue maximizes per-second information gain under lighting changes, motivating its selection. The modular design attains reliable navigation without metric SLAM or large-scale learning, and seamlessly accommodates future perception or policy upgrades.

基于特征的RGB-D姿态估计和拓扑地图的高效学习机器人导航。
强大的室内机器人导航通常需要昂贵的传感器或大量的训练数据。我们提出了一种具有成本效益的RGB-D导航管道,该管道将基于特征的相对姿态估计与轻量级多层感知器(MLP)策略相结合。从人类驱动的遍历中提取的RGB-D关键帧形成拓扑图的节点;当视觉相似性和几何运动学约束同时满足时,添加边缘。在自主过程中,LightGlue特征和SVD为活动关键帧提供六自由度的相对姿态,MLP预测四种离散动作中的一种。低视觉相似性或检测到的障碍物触发图形编辑和Dijkstra实时重新规划。在四个Habitat-Sim环境中的八个任务中,代理覆盖了190.44 m,在需要时重新规划,并且在商用硬件上运行时始终在距离目标0.1 m内停止。对Multi-Illumination数据集的信息论分析表明,LightGlue在光照变化下最大限度地提高了每秒的信息增益,从而激发了它的选择。模块化设计实现了可靠的导航,无需度量SLAM或大规模学习,并无缝适应未来的感知或策略升级。
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来源期刊
Entropy
Entropy PHYSICS, MULTIDISCIPLINARY-
CiteScore
4.90
自引率
11.10%
发文量
1580
审稿时长
21.05 days
期刊介绍: Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.
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