FLAF: Focal Line and Feature-Constrained Active View Planning for Visual Teach and Repeat

Abstract

This paper presents FLAF, a focal line and feature-constrained active view planning method for autonomous orientation adjustment of a rotatable active camera during mobile robot navigation. FLAF is built on a visual teach-and-repeat (VT&R) system, which enables robots to cruise various paths that fulfill many daily autonomous navigation requirements. The VT&R system integrates Visual Simultaneous Localization and Mapping (VSLAM) with trajectory following. However, tracking failures in feature-based VSLAM, particularly in textureless regions common in human-made environments, poses a significant challenge to real-world VT&R deployment. To address this, the proposed view planner is integrated into a feature-based VSLAM system, creating an active camerabased VSLAM (AC-SLAM) solution that mitigates tracking failures. Our system features a Pan-Tilt Unit (PTU)-based active camera mounted on a mobile robot. FLAF actively directs the camera toward more map points during path learning and toward more feature-identifiable map points while following the learned trajectory. Using FLAF, the AC-SLAM system constructs a complete path map during teaching and maintains stable localization during repeating. Experimental results in real scenarios show that FLAF significantly outperforms existing methods by accounting for feature identifiability, particularly the view angle of the features. During effectively dealing with low-texture regions in active view planning, considering feature identifiability enables our active VT&R system to perform well in challenging environments.