紧耦合的LiDAR-IMU-轮式里程计与基于因子图优化的在线神经运动学模型学习
Tightly-Coupled LiDAR-IMU-Wheel Odometry with an Online Neural Kinematic Model Learning via Factor Graph Optimization
AI总结：
针对点云退化与运动学模型非线性误差，提出基于因子图在线训练神经网络的LiDAR-IMU-轮式紧耦合里程计，提升复杂环境下的估计鲁棒性。
摘要：
缺乏几何特征的环境（如隧道和长直走廊）对基于LiDAR的里程计算法构成挑战，因为LiDAR点云在此类环境中会发生退化。对于轮式机器人，轮式运动学模型（即轮式里程计）可以提高里程计估计的可靠性。然而，在滑移转向机器人的情况下，运动学模型会受到复杂运动（如车轮打滑、侧向移动）的影响，特别是因为这种机器人模型通过车轮滑移来实现旋转。此外，当车轮打滑较大（如漂移）时，这些误差会非线性变化，并受地形相关参数的影响。为了同时解决点云退化和运动学模型误差，我们开发了一种LiDAR-IMU-轮式里程计算法，该算法结合了在线训练的神经网络，用于学习具有非线性的轮式机器人的运动学模型。我们提出在因子图上与机器人状态一起在线训练神经网络，使基于学习的运动学模型能够适应当前地形条件。所提出的方法在统一因子图上联合解决神经网络的在线训练和LiDAR-IMU-轮式里程计问题，以保持所有约束的一致性。通过实验，我们首先验证了所提出的网络能够适应变化的环境，从而在不同环境中实现准确的里程计估计。然后，我们确认了所提出的里程计算法对点云退化和运动学模型的非线性（如漂移引起的大车轮打滑）具有鲁棒性。摘要视频可在此处获取：https://www.youtube.com/watch?v=CvRVhdda7Cw
英文摘要：
Environments lacking geometric features (e.g., tunnels and long straight corridors) are challenging for LiDAR-based odometry algorithms because LiDAR point clouds degenerate in such environments. For wheeled robots, a wheel kinematic model (i.e., wheel odometry) can improve the reliability of the odometry estimation. However, the kinematic model suffers from complex motions (e.g., wheel slippage, lateral movement) in the case of skid-steering robots particularly because this robot model rotates by skidding its wheels. Furthermore, these errors change nonlinearly when the wheel slippage is large (e.g., drifting) and are subject to terrain-dependent parameters. To simultaneously tackle point cloud degeneration and the kinematic model errors, we developed a LiDAR-IMU-wheel odometry algorithm incorporating online training of a neural network that learns the kinematic model of wheeled robots with nonlinearity. We propose to train the neural network online on a factor graph along with robot states, allowing the learning-based kinematic model to adapt to the current terrain condition. The proposed method jointly solves online training of the neural network and LiDAR-IMU-wheel odometry on a unified factor graph to retain the consistency of all those constraints. Through experiments, we first verified that the proposed network adapted to a changing environment, resulting in an accurate odometry estimation across different environments. We then confirmed that the proposed odometry estimation algorithm was robust against point cloud degeneration and nonlinearity (e.g., large wheel slippage by drifting) of the kinematic model. The summary video is available here: https://www.youtube.com/watch?v=CvRVhdda7Cw
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
