As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research. In particular, event cameras have emerged as a compelling alternative to frame-based cameras in low-power and latency-demanding applications. To enable event cameras to operate alongside staple sensors like lidar in perception tasks, we propose a direct, temporally-decoupled extrinsic calibration method between event cameras and lidars. The high dynamic range, high temporal resolution, and low-latency operation of event cameras are exploited to directly register lidar laser returns, allowing information-based correlation methods to optimize for the 6-DoF extrinsic calibration between the two sensors. This paper presents the first direct calibration method between event cameras and lidars, removing dependencies on frame-based camera intermediaries and/or highly-accurate hand measurements. Code will be made publicly available.
翻译:随着神经形态技术的成熟,对机器人和自主车辆系统的应用已成为一个积极研究领域,特别是,事件摄像机已成为低功率和低悬浮要求应用中基于框架的摄像机的一种令人信服的替代方法。为了使事件摄像机能够与Lidar等主传感器一起运行,我们提议在事件摄像机和Lidars之间采用一种直接的、暂时脱钩的外部校准方法。事件摄像机的高动态范围、高时间分辨率和低时间操作率的相机被用来直接登记利达尔激光回报,允许两种传感器之间基于信息的相关方法优化6-DoF的外部校准。本文介绍了事件摄像机和Lidars之间的第一种直接校准方法,消除基于框架的相机中介和(或)高度精准手测量的依赖性。代码将公布于众。