Robust Monocular Visual Teach and Repeat Aided by Local Ground Planarity and Color-constant Imagery

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• 作者：Lee Clement ; Jonathan Kelly and Timothy D. Barfoot
• 刊名：Journal of Field Robotics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：34
• 期：1
• 页码：74-97
• 全文大小：2934K
• ISSN：1556-4967

文摘

Visual Teach and Repeat (VT&R) allows an autonomous vehicle to accurately repeat a previously traversed route using only vision sensors. Most VT&R systems rely on natively three-dimensional (3D) sensors such as stereo cameras for mapping and localization, but many existing mobile robots are equipped with only 2D monocular vision, typically for teleoperation. In this paper, we extend VT&R to the most basic sensor configuration—a single monocular camera. We show that kilometer-scale route repetition can be achieved with centimeter-level accuracy by approximating the local ground surface near the vehicle as a plane with some uncertainty. This allows our system to recover absolute scale from the known position and orientation of the camera relative to the vehicle, which simplifies threshold-based outlier rejection and the estimation and control of lateral path-tracking error—essential components of high-accuracy route repetition. We enhance the robustness of our monocular VT&R system to common failure cases through the use of color-constant imagery, which provides it with a degree of resistance to lighting changes and moving shadows where keypoint matching on standard gray images tends to struggle. Through extensive testing on a combined 30 km of autonomous navigation data collected on multiple vehicles in a variety of highly nonplanar terrestrial and planetary-analogue environments, we demonstrate that our system is capable of achieving route-repetition accuracy on par with its stereo counterpart, with only a modest tradeoff in robustness.