Event cameras continue to attract interest due to desirable characteristics such as high dynamic range, low latency, virtually no motion blur, and high energy efficiency. One of the potential applications that would benefit from these characteristics lies in visual place recognition for robot localization, i.e. matching a query observation to the corresponding reference place in the database. In this letter, we explore the distinctiveness of event streams from a small subset of pixels (in the tens or hundreds). We demonstrate that the absolute difference in the number of events at those pixel locations accumulated into event frames can be sufficient for the place recognition task, when pixels that display large variations in the reference set are used. Using such sparse (over image coordinates) but varying (variance over the number of events per pixel location) pixels enables frequent and computationally cheap updates of the location estimates. Furthermore, when event frames contain a constant number of events, our method takes full advantage of the event-driven nature of the sensory stream and displays promising robustness to changes in velocity. We evaluate our proposed approach on the Brisbane-Event-VPR dataset in an outdoor driving scenario, as well as the newly contributed indoor QCR-Event-VPR dataset that was captured with a DAVIS346 camera mounted on a mobile robotic platform. Our results show that our approach achieves competitive performance when compared to several baseline methods on those datasets, and is particularly well suited for compute- and energy-constrained platforms such as interplanetary rovers.
翻译:活动相机继续吸引人们的兴趣,因为具有高动态范围、低延迟度、几乎没有运动模糊度、高能效等理想特性。这些特性的潜在应用之一在于对机器人本地化的视觉识别,即将查询观测与数据库中相应的参考位置相匹配。在这封信中,我们探索了少数像素组(数十或数百个)的活动流的独特性。我们表明,这些像素地点累积到事件框架中的事件数量绝对不同,对于定位任务来说,只要使用在参考集中显示巨大变异的像素,就足够了。使用这种稀疏(超版图像坐标)但变化不一的机器人本地化(相对于每像素位置的事件数量的不同)像素等,可以经常和计算地更新数据库中的相应参考点。此外,当事件框架包含固定事件数量时,我们的方法充分利用了感官流事件驱动的性质,并展示了对变化速度变化的可靠度。我们评估了我们在Bris-Even-VPR平台上拟议的确认重大变异的像仪方法。使用这种稀少(相对于每像标数事件数量)的图像坐标平台上,这些移动数据是最新显示我们内部变异性图像平台上的最新数据,作为最新数据显示的S-CRCD-CRA-CRA-CR-C-CR,这些最新数据是最新数据,这是最新数据,这些最新数据显示的新数据,在室-CRDR-C-C-C-C-CR-C-C-C-C-C-C-C-C-C-C-C-S-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-S-S-S-S-S-S-S-S-S-C-C-C-C-C-S-S-S-S-S-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-C-S-C-C-C-C-C-S-S-S-S-S-S-S-S-S-S-S-S-C-C-C-C-C-C-C-C-C-C-