Search Results (2)

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. Full article

Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively. Full article