论文标题
一个分散的框架，用于同时校准，本地化和映射多个激光痛
A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs
论文作者
论文摘要
LiDar在自动驾驶工具中扮演着越来越重要的角色，以进行异议检测，自定位和映射。由于缺乏足够的几何形状功能，尤其是对于具有较小的视野（FOV）（FOV）的固态灯光射击，因此单一的痛通常患有硬件故障（例如，由于刺激性车辆环境（例如，温度，振动等）或性能退化，由于刺激性的车辆环境（例如，温度，振动等）或性能退化，经常患有硬件故障（例如暂时丧失连接）。为了改善自定位和映射中的系统鲁棒性和性能，我们开发了一个分散的框架，用于同时校准，本地化和与多个激光痛。我们提出的框架基于扩展的Kalman过滤器（EKF），但专门用于分散实施。这样的实现可能会在为每个激光雷达专用的较小计算设备或资源之间分配密集的计算，并消除单点故障问题。然后，这种分散的配方将在携带5个低成本激光射击的无人接地车（UGV）上实施，并在城市环境中以130万美元的价格移动。实验结果表明，所提出的方法可以成功，同时估计车辆状态（即姿势和速度）和所有LIDAR外部参数。我们收集的两个数据集的本地化精度高达0.2％。为了分享我们的发现并为社区做出贡献，同时使读者能够验证我们的作品，我们将在GitHub上发布所有源代码和硬件设计蓝图。
LiDAR is playing a more and more essential role in autonomous driving vehicles for objection detection, self localization and mapping. A single LiDAR frequently suffers from hardware failure (e.g., temporary loss of connection) due to the harsh vehicle environment (e.g., temperature, vibration, etc.), or performance degradation due to the lack of sufficient geometry features, especially for solid-state LiDARs with small field of view (FoV). To improve the system robustness and performance in self-localization and mapping, we develop a decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs. Our proposed framework is based on an extended Kalman filter (EKF), but is specially formulated for decentralized implementation. Such an implementation could potentially distribute the intensive computation among smaller computing devices or resources dedicated for each LiDAR and remove the single point of failure problem. Then this decentralized formulation is implemented on an unmanned ground vehicle (UGV) carrying 5 low-cost LiDARs and moving at $1.3m/s$ in urban environments. Experiment results show that the proposed method can successfully and simultaneously estimate the vehicle state (i.e., pose and velocity) and all LiDAR extrinsic parameters. The localization accuracy is up to 0.2% on the two datasets we collected. To share our findings and to make contributions to the community, meanwhile enable the readers to verify our work, we will release all our source codes and hardware design blueprint on our Github.
