Monocular Visual Teach and Repeat Aided by Local Ground Planarity
Lee Clement, Jonathan Kelly, Timothy D. Barfoot

TL;DR
This paper presents a monocular vision system for high-accuracy route repetition in autonomous vehicles by leveraging ground planarity assumptions to recover absolute scale, enabling centimetre-level accuracy over long distances without additional sensors.
Contribution
It introduces a monocular visual Teach and Repeat system that estimates absolute scale using ground planarity, allowing kilometre-scale route repetition with high accuracy.
Findings
Achieves centimetre-level accuracy over 4.3 km
Performs comparably to stereo-based systems in non-planar terrain
Enables high-precision autonomous navigation with only monocular cameras
Abstract
Visual Teach and Repeat (VT\&R) allows an autonomous vehicle to repeat a previously traversed route without a global positioning system. Existing implementations of VT\&R typically rely on 3D sensors such as stereo cameras for mapping and localization, but many mobile robots are equipped with only 2D monocular vision for tasks such as teleoperated bomb disposal. While simultaneous localization and mapping (SLAM) algorithms exist that can recover 3D structure and motion from monocular images, the scale ambiguity inherent in these methods complicates the estimation and control of lateral path-tracking error, which is essential for achieving high-accuracy path following. In this paper, we propose a monocular vision pipeline that enables kilometre-scale route repetition with centimetre-level accuracy by approximating the ground surface near the vehicle as planar (with some uncertainty) and…
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