论文标题
使用静态对象信息的自动驾驶汽车的多传感器3D LIDAR系统的有效外部校准
Efficient Extrinsic Calibration of Multi-Sensor 3D LiDAR Systems for Autonomous Vehicles using Static Objects Information
论文作者
论文摘要
对于自动驾驶汽车，通过融合不同的传感器数据流来感知周围环境并建立环境的整体表示的能力是基本的。为此，需要准确确定所有传感器的姿势。传统的校准方法基于：1）使用专门为受控环境中的校准目的设计的目标，2）在穿越未知但静态环境时优化收集的点云的质量指标，或者3）在沿着运动路径沿着运动路径满足特殊要求的每次效率增量运动观察中优化匹配。但是，在实际情况下，这些方法的在线适用性可能会受到限制，因为它们通常是高度动态的，包含退化路径，并且需要快速计算。在本文中，我们提出了一种方法，该方法通过将校准问题制定为所有传感器校准的关节但结构化的优化问题来应对其中的某些挑战，这些问题将其作为输入的摘要，其中包括由地面点和极点检测组成的点云信息。我们在一组LiDAR模拟和城市旅行中的真实数据进行了一组实验中，证明了所提出方法的结果的效率和质量。
For an autonomous vehicle, the ability to sense its surroundings and to build an overall representation of the environment by fusing different sensor data streams is fundamental. To this end, the poses of all sensors need to be accurately determined. Traditional calibration methods are based on: 1) using targets specifically designed for calibration purposes in controlled environments, 2) optimizing a quality metric of the point clouds collected while traversing an unknown but static environment, or 3) optimizing the match among per-sensor incremental motion observations along a motion path fulfilling special requirements. In real scenarios, however, the online applicability of these methods can be limited, as they are typically highly dynamic, contain degenerate paths, and require fast computations. In this paper, we propose an approach that tackles some of these challenges by formulating the calibration problem as a joint but structured optimization problem of all sensor calibrations that takes as input a summary of the point cloud information consisting of ground points and pole detections. We demonstrate the efficiency and quality of the results of the proposed approach in a set of experiments with LiDAR simulation and real data from an urban trip.
