SPOT：基于点云的立体视觉位置识别用于相似和对立视角
SPOT: Point Cloud Based Stereo Visual Place Recognition for Similar and Opposing Viewpoints
AI总结：
本文提出SPOT技术，利用立体视觉里程计估计的结构进行对立视角视觉位置识别，通过双距离矩阵序列匹配方法提升识别精度，实验表明在不同光照条件下，SPOT在对立视角识别中达到91.7%的召回率，且存储和运行效率优于现有方法。
摘要：
在返程途中从对立视角识别地点是人类司机的常见经历。然而，机器人领域中，使用有限视野摄像头在180度旋转下实现视觉位置识别（VPR）的能力却极具挑战性。为解决此问题，本文提出了Same Place Opposing Trajectory（SPOT），一种依赖于立体视觉里程计估计结构的对立视角VPR技术。该方法结合了最近的激光雷达描述符进展，并利用新颖的双（相似和对立）距离矩阵序列匹配方法。我们评估了SPOT在公开数据集上的表现，该数据集包含在相似和对立方向上驾驶6.7-7.6公里的路线，并在不同光照条件下进行测试。所提算法在对立视角情况下实现了高达91.7%的召回率（100%精度），所需存储空间少于所有测试基线，且运行速度优于所有但一个基线。此外，所提方法不事先假设视角是相似还是对立，并在相似视角情况下也表现出竞争力。
英文摘要：
Recognizing places from an opposing viewpoint during a return trip is a common experience for human drivers. However, the analogous robotics capability, visual place recognition (VPR) with limited field of view cameras under 180 degree rotations, has proven to be challenging to achieve. To address this problem, this paper presents Same Place Opposing Trajectory (SPOT), a technique for opposing viewpoint VPR that relies exclusively on structure estimated through stereo visual odometry (VO). The method extends recent advances in lidar descriptors and utilizes a novel double (similar and opposing) distance matrix sequence matching method. We evaluate SPOT on a publicly available dataset with 6.7-7.6 km routes driven in similar and opposing directions under various lighting conditions. The proposed algorithm demonstrates remarkable improvement over the state-of-the-art, achieving up to 91.7% recall at 100% precision in opposing viewpoint cases, while requiring less storage than all baselines tested and running faster than all but one. Moreover, the proposed method assumes no a priori knowledge of whether the viewpoint is similar or opposing, and also demonstrates competitive performance in similar viewpoint cases.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
