Robot Active Vision-Based Path Planning for Localization Improvement in Indoor Environments

Sotirios Barlakas, Dimitrios Alexiou, Kosmas Tsiakas, Dimitrios Katsatos, Ioannis Kostavelis, Dimitrios Giakoumis, Antonios Gasteratos, Dimitris Tzovaras

Keywords: Robotics and Embodied AI, Vision and Learning, Path Planning, Mobile Robots, Robot Navigation, Indoor Navigation, Artificial Potential Field, Localization, SLAM, Active Navigation, Model Predictive Control

TL;DR: An active vision-based navigation framework that uses localizability-aware path planning and adaptive control to improve localization and navigation of mobile robots in visually challenging indoor environments, evaluated in real-world conditions.

Abstract:

Reliable and robust navigation of autonomous mobile robots in indoor environments faces significant challenges due to the absence of GPS, visual degradation, repetitive structures, illumination variations, and low texture. These factors adversely affect localization systems. Current robots often use a uniform navigation approach, regardless of the varying localization uncertainties within different indoor environments. In this paper, we propose a holistic, active vision-based path planning method that produces efficient trajectories, aiming to minimize localization error and enhance navigation performance. Specifically, we utilize a 3D model of an indoor environment to derive an Artificial Potential Field (APF) with its associated localizability scores that encapsulate both visual features’ richness and fiducial markers’ placement. APF is employed to direct a Kinematically Constrained Bi-directional Rapidly Exploring Random Tree (KB-RRT) planner towards the calculation of optimal paths, prioritizing high localization areas. Subsequently, we use an online weight-adaptive MPC-based approach that, apart from robust path planning and obstacle avoidance, guides the robot towards areas with the most robust visual features in order to further refine the localization error. The proposed framework has been extensively tested in both simulation and real-world experiments with a mobile robot in a visually challenging indoor environment.

The paper has been presented at 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), which was held on October 14-18, 2024 at Abu Dhabi, UAE.

It is available on IEEE Xplore through the following link: https://ieeexplore.ieee.org/abstract/document/10802041

Submission Number: 61