Sheng Diwei, Chai Yuxiang, Li Xinru, Feng Chen, Lin Jianzhe, Silva Claudio, Rizzo John-Ross
New York University, Brooklyn, NY 11201, USA.
Rep U S. 2021 Sep-Oct;2021:9773-9779. doi: 10.1109/iros51168.2021.9636640. Epub 2021 Dec 16.
Visual place recognition (VPR) is critical in not only localization and mapping for autonomous driving vehicles, but also assistive navigation for the visually impaired population. To enable a long-term VPR system on a large scale, several challenges need to be addressed. First, different applications could require different image view directions, such as front views for self-driving cars while side views for the low vision people. Second, VPR in metropolitan scenes can often cause privacy concerns due to the imaging of pedestrian and vehicle identity information, calling for the need for data anonymization before VPR queries and database construction. Both factors could lead to VPR performance variations that are not well understood yet. To study their influences, we present the NYU-VPR dataset that contains more than 200,000 images over a 2km×2km area near the New York University campus, taken within the whole year of 2016. We present benchmark results on several popular VPR algorithms showing that side views are significantly more challenging for current VPR methods while the influence of data anonymization is almost negligible, together with our hypothetical explanations and in-depth analysis.
视觉场所识别（VPR）不仅对自动驾驶车辆的定位和地图构建至关重要，对于视障人群的辅助导航也很关键。为了大规模启用长期VPR系统，需要解决几个挑战。首先，不同的应用可能需要不同的图像视角方向，比如自动驾驶汽车需要前视图，而视力低下的人则需要侧视图。其次，由于行人及车辆身份信息成像的原因，大都市场景中的VPR常常会引发隐私问题，这就要求在VPR查询和数据库构建之前对数据进行匿名化处理。这两个因素都可能导致尚未被充分理解的VPR性能变化。为了研究它们的影响，我们展示了NYU-VPR数据集，该数据集包含2016年全年在纽约大学校园附近2公里×2公里区域内拍摄的20多万张图像。我们展示了几种流行VPR算法的基准测试结果，表明侧视图对当前VPR方法来说挑战显著更大，而数据匿名化的影响几乎可以忽略不计，同时还有我们的假设解释和深入分析。
