论文标题
基于事件的视觉位置识别与颞窗的合奏
Event-based visual place recognition with ensembles of temporal windows
论文作者
论文摘要
事件摄像机是由生物启发的传感器，能够提供低潜伏期和高动态范围的连续事件流。由于单个事件仅包含有关特定像素处亮度变化的有限信息，因此事件通常会积累到时空窗口中以进行进一步处理。但是，最佳窗口长度取决于场景，相机运动，执行任务以及其他因素。在这项研究中，我们开发了一种基于合奏的新型方案，用于结合并行处理的不同长度的时间窗口。对于这种方法增加的计算要求不实用的应用程序，我们还引入了一种新的“近似”合奏方案，该方案在不损害集合方法提供的原始性能增长的情况下，实现了重大的计算效率。我们在Visual Place识别（VPR）任务上演示了我们的合奏方案，并引入了新的Brisbane-Event-VPR数据集，并使用使用Davis346彩色事件摄像机捕获的带注释的录音。我们表明，我们提出的整体方案显着胜过所有单窗基线和常规模型的集合，而与VPR管道中使用的图像重建和特征提取方法无关，并评估哪种合奏组合技术的表现最好。这些结果证明了集合方案在VPR域中的事件摄像机处理的重大好处，并且可能与其他相关过程相关，包括功能跟踪，视觉惯性探测器和驾驶方向预测。
Event cameras are bio-inspired sensors capable of providing a continuous stream of events with low latency and high dynamic range. As a single event only carries limited information about the brightness change at a particular pixel, events are commonly accumulated into spatio-temporal windows for further processing. However, the optimal window length varies depending on the scene, camera motion, the task being performed, and other factors. In this research, we develop a novel ensemble-based scheme for combining temporal windows of varying lengths that are processed in parallel. For applications where the increased computational requirements of this approach are not practical, we also introduce a new "approximate" ensemble scheme that achieves significant computational efficiencies without unduly compromising the original performance gains provided by the ensemble approach. We demonstrate our ensemble scheme on the visual place recognition (VPR) task, introducing a new Brisbane-Event-VPR dataset with annotated recordings captured using a DAVIS346 color event camera. We show that our proposed ensemble scheme significantly outperforms all the single-window baselines and conventional model-based ensembles, irrespective of the image reconstruction and feature extraction methods used in the VPR pipeline, and evaluate which ensemble combination technique performs best. These results demonstrate the significant benefits of ensemble schemes for event camera processing in the VPR domain and may have relevance to other related processes, including feature tracking, visual-inertial odometry, and steering prediction in driving.
