论文标题
汽车雷达的连续时间持续惯性进程
Continuous-time Radar-inertial Odometry for Automotive Radars
论文作者
论文摘要
我们提出了一种使用连续时间框架来融合来自多个汽车雷达和惯性测量单元（IMU）的测量值的方法，该方法使用连续的时间框架进行融合。与摄像机和激光雷达传感器不同，不利的天气条件对雷达传感器的操作性能没有重大影响。雷达在这种情况下的鲁棒性以及乘用车上雷达的越来越多的雷达促使我们研究了雷达对自我运动估计的使用。连续的时间轨迹表示不仅是作为一个框架来实现异质和异步的多传感器融合，而且还可以通过沿轨迹的任何给定时间计算封闭形式的姿势及其导数来促进有效的优化。我们将连续的时间估计与来自离散时间惯性探测方法的连续时间估计进行比较，并表明我们的连续时间方法的表现优于离散时间方法。据我们所知，这是第一次将连续的时间框架应用于雷达惯性探测器。
We present an approach for radar-inertial odometry which uses a continuous-time framework to fuse measurements from multiple automotive radars and an inertial measurement unit (IMU). Adverse weather conditions do not have a significant impact on the operating performance of radar sensors unlike that of camera and LiDAR sensors. Radar's robustness in such conditions and the increasing prevalence of radars on passenger vehicles motivate us to look at the use of radar for ego-motion estimation. A continuous-time trajectory representation is applied not only as a framework to enable heterogeneous and asynchronous multi-sensor fusion, but also, to facilitate efficient optimization by being able to compute poses and their derivatives in closed-form and at any given time along the trajectory. We compare our continuous-time estimates to those from a discrete-time radar-inertial odometry approach and show that our continuous-time method outperforms the discrete-time method. To the best of our knowledge, this is the first time a continuous-time framework has been applied to radar-inertial odometry.
