Image-Based Trajectory Tracking through Unknown Environments without Absolute Positioning

TL;DR

This paper introduces a stereo image-based visual servoing system enabling a non-holonomic robot to track trajectories in unknown environments without relying on absolute positioning or pre-existing maps.

Contribution

It presents a novel feature-based, indirect SLAM method that provides depth-estimated features for trajectory tracking without external pose data.

Findings

Trajectory servoing outperforms pose-based feedback in tracking accuracy.

System successfully navigates unknown environments without absolute positioning.

Experimental results demonstrate improved performance in short and long-distance navigation.

Abstract

This paper describes a stereo image-based visual servoing system for trajectory tracking by a non-holonomic robot without externally derived pose information nor a known visual map of the environment. It is called trajectory servoing. The critical component is a feature-based, indirect Simultaneous Localization And Mapping (SLAM) method to provide a pool of available features with estimated depth, so that they may be propagated forward in time to generate image feature trajectories for visual servoing. Short and long distance experiments show the benefits of trajectory servoing for navigating unknown areas without absolute positioning. Empirically, trajectory servoing has better trajectory tracking performance than pose-based feedback when both rely on the same underlying SLAM system.