无需标定板，利用地面分割实现无人机惯性与深度相机的高精度外参标定
Ground Plane-Aided Extrinsic Calibration of Inertial and RGB-D Sensors for Uncrewed Aerial Vehicles
- 通过深度学习分割图像中的地面点，提取其法向量作为约束
- 结合加速度计测得的重力方向，鲁棒估计传感器外参
- 无需标定板和初始参数，性能媲美专业工具
惯性传感器（如IMU）与相机的精确外参标定对无人机轨迹估计至关重要。现有方法通常依赖专用设备、平面标靶及初始参数估计。本文提出一种针对配备IMU和RGB-D相机的无人机的无标靶标定方法。利用基于深度学习的地面分割技术，从RGB-D图像的深度通道中提取地面点，并估计其法向量。利用已知的地面法向量与加速度计坐标系中测得的重力向量，通过鲁棒估计方法求解外参。实验表明，该方法优于MATLAB工具箱，且性能与Kalibr相当，无需使用特殊棋盘靶。
原文摘要 · Abstract (English)
Accurate extrinsic calibration of inertial sensors, such as Inertial Measurement Units (IMUs) and cameras is crucial for trajectory estimation of Uncrewed Aerial Vehicles (UAVs). While numerous calibration methods have been proposed, these techniques often rely on specialized equipment, planar targets, and an initial estimate of the calibration parameters. In this research, we propose a targetless calibration method designed for UAVs equipped with IMUs and RGB-Depth (RGB-D) cameras. Our approach leverages deep-learning-based floor-segmentation to extract ground points from the depth channel of RGB-D images. Subsequently, the normal vector to these points is estimated. The known orientation of the normal to the floor segment and the gravity vector sensed in the accelerometer's frame are utilized in a robust estimation approach to estimate the extrinsic calibration parameters. We illustrate that the developed method outperforms MATLAB's Toolboxes and exhibits similar performance to Kalibr without the use of specialized checkerboard targets.
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