论文标题
Devo：在具有挑战性的条件下，深度事件相机的视觉探光仪
DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions
论文作者
论文摘要
我们提供了一个新颖的实时视觉进程框架，以实现深度和高分辨率事件摄像头的立体设置。我们的框架可以在具有挑战性的场景中平衡准确性和鲁棒性与计算效率方面的效果。我们将传统的基于边缘的半密度视觉探光仪扩展到从事件流获得的时间表面图。半密度的深度图是通过翘曲外部校准深度相机的相应深度值而产生的。跟踪模块通过有效的，几何半密度的3D-2D边比对齐来更新相机的姿势。在各种条件下捕获的公共和自收集的数据集中，我们的方法得到了验证。我们表明，所提出的方法在常规条件下的最先进的RGB-D摄像头替代方案可比性，并且在诸如高动力学或低照明之类的挑战性条件下最终优于表现。
We present a novel real-time visual odometry framework for a stereo setup of a depth and high-resolution event camera. Our framework balances accuracy and robustness against computational efficiency towards strong performance in challenging scenarios. We extend conventional edge-based semi-dense visual odometry towards time-surface maps obtained from event streams. Semi-dense depth maps are generated by warping the corresponding depth values of the extrinsically calibrated depth camera. The tracking module updates the camera pose through efficient, geometric semi-dense 3D-2D edge alignment. Our approach is validated on both public and self-collected datasets captured under various conditions. We show that the proposed method performs comparable to state-of-the-art RGB-D camera-based alternatives in regular conditions, and eventually outperforms in challenging conditions such as high dynamics or low illumination.
