Recently, the progress in the radar sensing technology consisting in the miniaturization of the packages and increase in measuring precision has drawn the interest of the robotics research community. Indeed, a crucial task enabling autonomy in robotics is to precisely determine the pose of the robot in space. To fulfill this task sensor fusion algorithms are often used, in which data from one or several exteroceptive sensors like, for example, LiDAR, camera, laser ranging sensor or GNSS are fused together with the Inertial Measurement Unit (IMU) measurements to obtain an estimate of the navigation states of the robot. Nonetheless, owing to their particular sensing principles, some exteroceptive sensors are often incapacitated in extreme environmental conditions, like extreme illumination or presence of fine particles in the environment like smoke or fog. Radars are largely immune to aforementioned factors thanks to the characteristics of electromagnetic waves they use. In this thesis, we present Radar-Inertial Odometry (RIO) algorithms to fuse the information from IMU and radar in order to estimate the navigation states of a (Uncrewed Aerial Vehicle) UAV capable of running on a portable resource-constrained embedded computer in real-time and making use of inexpensive, consumer-grade sensors. We present novel RIO approaches relying on the multi-state tightly-coupled Extended Kalman Filter (EKF) and Factor Graphs (FG) fusing instantaneous velocities of and distances to 3D points delivered by a lightweight, low-cost, off-the-shelf Frequency Modulated Continuous Wave (FMCW) radar with IMU readings. We also show a novel way to exploit advances in deep learning to retrieve 3D point correspondences in sparse and noisy radar point clouds.

翻译：近年来，雷达传感技术在封装小型化和测量精度提升方面的进展引起了机器人研究界的广泛关注。事实上，实现机器人自主性的一个关键任务是精确确定其在空间中的位姿。为完成此任务，通常采用传感器融合算法，将来自一个或多个外感受传感器（如LiDAR、相机、激光测距传感器或GNSS）的数据与惯性测量单元（IMU）的测量值相融合，以获得机器人导航状态的估计。然而，由于其特定的传感原理，某些外感受传感器在极端环境条件下（如极端光照或环境中存在烟雾、雾气等细微颗粒物）常常无法正常工作。雷达得益于其所使用的电磁波特性，对上述因素基本不受影响。在本论文中，我们提出了雷达-惯性里程计（RIO）算法，用于融合IMU和雷达的信息，以估计能够在便携式资源受限嵌入式计算机上实时运行、并使用低成本消费级传感器的无人飞行器（UAV）的导航状态。我们提出了新颖的RIO方法，该方法基于多状态紧耦合扩展卡尔曼滤波器（EKF）和因子图（FG），融合了由轻量级、低成本、现成的调频连续波（FMCW）雷达提供的3D点瞬时速度与距离信息以及IMU读数。我们还展示了一种利用深度学习进展的新颖方法，用于在稀疏且噪声较大的雷达点云中检索3D点对应关系。