OmniCalib：人形机器人的无靶标、任务结构化自标定
OmniCalib: Target-Free, Task-Structured Self-Calibration for Humanoid Robots
AI总结：
OmniCalib提出一种无靶标、任务结构化的自标定方法，利用机器人自身运动与机载感知，标定人形机器人全身关节零位与相机外参，实验验证精度高且无需外部基准。
AI中文摘要：
装配、磨损和部件更换会扰动人形机器人CAD模型所编码的传感器外参和关节零位。现有流程仅标定单个传感器对或需要外部基准标记。仅利用机器人自身运动与机载感知，我们提出OmniCalib，一种无靶标工作流，可标定完整上肢——全部14个手臂关节零位以及腕部和胸部两个相机的外参——以及下肢和多相机头部装置。每个模块将机器人自身任务与参数块匹配，检查可观测性，并仅将受支持的修正写入CAD模型。我们的深度ICP方法无需任何标定靶标即可恢复全部14个手臂关节零位并标定所有RGB-D相机外参。相对于CAD，估计的外参修正为：左腕10.56毫米和1.74度，右腕6.33毫米和1.25度，胸部RGB-D相机9.81毫米和0.929度。ICP点到平面残差为2.09毫米。在相同注入偏移下，ICP和ArUco在0.1度编码器分辨率参考以下恢复全部14个关节零位。在AGIBOT A3 Ultra人形机器人上，四次静态双足支撑姿态恢复全部12个下肢关节零位偏移，RMS误差为0.063度。头部模块通过实时ROS变换树将多相机视觉里程计与腿部里程计及动态补偿相结合。仅使用平面行走，其在三个序列上达到平均SO(3)误差1.061度。最佳序列达到0.775度，与从丰富6自由度激励中获得的iKalibr的0.902度相当。装置相对角度重复性在0.140度以内。注入恢复和留出测试验证了每个可观测块。
英文摘要：
Assembly, wear, and component replacement perturb the sensor extrinsics and joint zeros encoded by a humanoid CAD model. Existing procedures calibrate one sensor pair or require external fiducials. Using only robot-native motion and onboard sensing, we present OmniCalib, a target-free workflow that calibrates the full upper limbs---all 14 arm joint zeros and the extrinsics of both wrist and chest cameras---as well as lower limbs and the multi-camera head rig. Each module matches a robot-native task to a parameter block, checks observability, and writes only supported corrections to the CAD model. Our depth ICP method recovers all 14 arm joint zeros and calibrates all RGB-D camera extrinsics without any calibration target. Relative to CAD, the estimated extrinsic corrections are 10.56 mm and 1.74 degrees for the left wrist, 6.33 mm and 1.25 degrees for the right wrist, and 9.81 mm and 0.929 degrees for the chest RGB-D camera. ICP point-to-plane residual is 2.09 mm. On the same injected offsets, ICP and ArUco recover all 14 joint zeros below the 0.1-degree encoder-resolution reference. On an AGIBOT A3 Ultra humanoid, four static double-support stances recover all 12 lower-limb joint-zero offsets injected with an RMS error of 0.063 degrees. The head module combines multi-camera visual odometry with legged odometry and dynamic compensation through the live ROS transform tree. Using only planar walking, it attains a mean SO(3) error of 1.061 degrees across three sequences. The best sequence reaches 0.775 degrees, competitive with iKalibr at 0.902 degrees from rich 6-DOF excitation. Rig-relative angles repeat within 0.140 degrees. Injection recovery and held-out tests validate each observable block.
