Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles

• URL: http://arxiv.org/abs/2203.00291v1
• Date: Tue, 1 Mar 2022 08:49:21 GMT
• Title: Efficient Globally-Optimal Correspondence-Less Visual Odometry for Planar Ground Vehicles
• Authors: Ling Gao, Junyan Su, Jiadi Cui, Xiangchen Zeng, Xin Peng, Laurent Kneip
• Abstract summary: We introduce the first globally-optimal, correspondence-less solution to plane-based Ackermann motion estimation. We prove its property of global optimality and analyse the impact of assuming a locally constant centre of rotation.
• Score: 23.910735789004075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The motion of planar ground vehicles is often non-holonomic, and as a result may be modelled by the 2 DoF Ackermann steering model. We analyse the feasibility of estimating such motion with a downward facing camera that exerts fronto-parallel motion with respect to the ground plane. This turns the motion estimation into a simple image registration problem in which we only have to identify a 2-parameter planar homography. However, one difficulty that arises from this setup is that ground-plane features are indistinctive and thus hard to match between successive views. We encountered this difficulty by introducing the first globally-optimal, correspondence-less solution to plane-based Ackermann motion estimation. The solution relies on the branch-and-bound optimisation technique. Through the low-dimensional parametrisation, a derivation of tight bounds, and an efficient implementation, we demonstrate how this technique is eventually amenable to accurate real-time motion estimation. We prove its property of global optimality and analyse the impact of assuming a locally constant centre of rotation. Our results on real data finally demonstrate a significant advantage over the more traditional, correspondence-based hypothesise-and-test schemes.

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