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SPOT: Point Cloud Based Stereo Visual Place Recognition for Similar and Opposing Viewpoints

2024-04-18 17:09:10
Spencer Carmichael, Rahul Agrawal, Ram Vasudevan, Katherine A. Skinner


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.

Abstract (translated)

在往返旅行中,从对方面临识别地点是一个常见的人类驾驶者的经历。然而,具有有限视野相机的视场机器人学能力(VPR)在实现方面被证明具有挑战性。为解决这个问题,本文提出了 Same Place Opposing Trajectory(SPOT),一种基于立体视觉惯性测量(VO)的反对观点VPR技术。该方法扩展了最近在激光描述符和双距离矩阵序列匹配方面的最新进展,并采用了一种新颖的double(相似和反对)距离矩阵序列匹配方法。我们在各种光照条件下,使用公开可用的数据集对SPOT进行了评估。与最先进的实现相比,所提出的算法在反对观点情况下实现了显著的提高,达到91.7%的召回率,而在100%精确度时,所需存储比所有测试基线都要少,并且比所有基线都要快。此外,所提出的假设没有预先知识来确定视点的相似性或反对性,并且在相似观点情况下也具有竞争力的性能。



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