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 of event camera research lies in visual place recognition for robot localization, where a query observation has to be matched 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:事件相机继续吸引人们的兴趣,因为具有高动态范围、低延度、几乎没有运动模糊和高能效等理想特性。事件相机研究的潜在应用之一是对机器人本地化的视觉识别,其中查询观测必须与数据库中的相应参考位置相匹配。在本信中,我们探索了小像素子组(数十或数百个)中事件流的独特性。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平台时,就足够了。使用这种稀疏(超版图像坐标)但变化不一的地方识别机器人本地化(每像素位置的事件数量不同)像素的视觉识别,从而能够经常和计算廉价地更新定位估计数。此外,当事件框架包含固定的事件数量时,我们的方法充分利用感应感应流的天体特性,并显示对速度变化的可靠度。我们关于Bris-V-VPR平台显示巨大变异的定位方法的建议方法,在室际驱动平台3 上,作为最新数据显示我们模型显示的机型模型,这些模型显示,这些最新数据是最新数据。