Vision-based relative pose estimation serves as a core technology for high-precision localization in autonomous vehicles and mobile platforms. To overcome the limitations of conventional three-view pose estimation methods that rely heavily on dense feature matching and incur high computational costs, this paper proposes an efficient three-point correspondence algorithm based on planar motion constraints. The method constructs trifocal tensor constraint equations and develops a linearized three-point solution framework, enabling rapid relative pose estimation using merely three corresponding points in three views. In simulation experiments, we systematically analyzed the robustness of the algorithm under complex conditions that included image noise, angular deviation, and vibration. The method was further validated in real-world scenarios using the KITTI public dataset. Experimental results demonstrate that under the condition of satisfying the planar motion assumption, the proposed method achieves significantly improved computational efficiency compared with traditional methods (including general three-view methods, two-view planar motion estimation methods, and classical two-view methods), with the single-solution time reduced by more than 80% compared to general three-view methods. In the public dataset, our algorithm achieves a median rotation estimation error of less than 0.0545 degrees and maintains a translation estimation error of less than 2.1319 degrees. The proposed method exhibits higher computational efficiency and better numerical stability compared to conventional algorithms. This research provides an effective pose estimation solution with real-time performance and high accuracy for planar motion platforms such as autonomous vehicles and indoor mobile robots, demonstrating substantial engineering application value.
Dai et al. (Mon,) studied this question.
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