Image-Based Trajectory Tracking through Unknown Environments without Absolute Positioning
This addresses navigation in unknown environments for robots, but it is incremental as it builds on existing SLAM and visual servoing techniques.
The paper tackles trajectory tracking for robots without external positioning or environmental maps by introducing a stereo image-based visual servoing system called trajectory servoing, which uses SLAM to propagate features for servoing and shows better tracking performance than pose-based methods in experiments.
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.