VILENS：面向全地形足式机器人的视觉、惯性、激光雷达与腿部里程计
VILENS: Visual, Inertial, Lidar, and Leg Odometry for All-Terrain Legged Robots
AI总结：
针对足式机器人单传感器里程计估计易退化的问题，提出基于因子图的多模态紧融合里程计系统VILENS，扩展可观测线速度偏差项，实验验证其精度和鲁棒性均显著优于现有松耦合方法。
摘要：
我们提出了视觉惯性激光雷达足式导航系统（VILENS），这是一种基于因子图的足式机器人里程计系统。其核心创新点在于对四种不同传感模态的紧融合，以在单个传感器会产生退化估计的场景下实现可靠运行。为最小化腿部里程计的漂移，我们在机器人状态中扩展了一个可在线估计的线速度偏差项。由于该预积分速度因子与视觉、激光雷达以及惯性测量单元（IMU）因子的紧融合，该偏差是可观测的。我们在不同的ANYmal四足机器人上开展了广泛的实验验证，总时长2小时，总行进距离1.8千米。实验包含在松散岩石、斜坡和泥浆上的动态运动，这些场景会带来滑移、地形变形等挑战；感知层面的挑战包括黑暗多尘的地下洞穴，以及开阔、特征匮乏的区域。结果表明，与当前最优的松耦合方法相比，该方法的平移误差平均降低62%，旋转误差平均降低51%。为验证其鲁棒性，VILENS还与感知控制器和局部路径规划器进行了集成。
英文摘要：
We present visual inertial lidar legged navigation system (VILENS), an odometry system for legged robots based on factor graphs. The key novelty is the tight fusion of four different sensor modalities to achieve reliable operation when the individual sensors would otherwise produce degenerate estimation. To minimize leg odometry drift, we extend the robot's state with a linear velocity bias term, which is estimated online. This bias is observable because of the tight fusion of this preintegrated velocity factor with vision, lidar, and inertial measurement unit (IMU) factors. Extensive experimental validation on different ANYmal quadruped robots is presented, for a total duration of 2 h and 1.8 km traveled. The experiments involved dynamic locomotion over loose rocks, slopes, and mud, which caused challenges such as slippage and terrain deformation. Perceptual challenges included dark and dusty underground caverns, and open and feature-deprived areas. We show an average improvement of 62% translational and 51% rotational errors compared to a state-of-the-art loosely coupled approach. To demonstrate its robustness, VILENS was also integrated with a perceptive controller and a local path planner.
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。
