Vision-Sensor-Assisted Probabilistic Localization Method for Indoor Environment

Abstract

Among the numerous indoor localization methods, Light-Detection-and-Ranging (LiDAR)-based probabilistic algorithms have been extensively applied to indoor localization due to their real-time performance and high accuracy. Nevertheless, these methods are challenged in symmetrical environments when tackling global localization and the robot kidnapping problem. In this paper, a novel hybrid method that combines visual and probabilistic localization results is proposed. Augmented Monte Carlo Localization (AMCL) is improved for position tracking continually. LiDAR-based measurements’ uncertainty is evaluated to incorporate discrete visual-based results; therefore, a better diversity of the particle can be maintained. The robot kidnapping problem can be detected and solved by preventing premature convergence of the particle filter. Extensive experiments were implemented to validate the robustness and accuracy performance. Meanwhile, the localization error was reduced from 30 mm to 9 mm during a 600 m tour.

A novel visual tracking scheme for unstructured indoor environments

“…The solution employs a heterogeneous visual sensor network composed of both fixed and pan-tilt-zoom (PTZ) cameras. Finally, Shi et al [34] proposed a novel hybrid method that combines visual and probabilistic localization results to improve the accuracy of indoor positioning systems. By combining these two techniques, the researchers were able to demonstrate improved accuracy in the determination of the location of a mobile robot within an indoor environment.…”