Visual-Inertial Odometry for Ground Robots with Dynamic Camera-IMU Extrinsic Calibration

Radar-Inertial Odometry (RIO) has emerged as a robust alternative to vision- and LiDAR-based odometry in challenging conditions such as low light, fog, featureless environments, or in adverse weather. However, many existing RIO approaches assume known radar-IMU extrinsic calibration or rely on sufficient motion excitation for online extrinsic estimation, while temporal misalignment between sensors is often neglected or treated independently. In this work, we present a RIO framework that performs joint online spatial and temporal calibration within a factor-graph optimization formulation, based on continuous-time modeling of inertial measurements using uniform cubic B-splines. The proposed continuous-time representation of acceleration and angular velocity accurately captures the asynchronous nature of radar-IMU measurements, enabling reliable convergence of both the temporal offset and extrinsic calibration parameters, without relying on scan matching, target tracking, or environment-specific assumptions.

Recent magnetometer-free inertial measurement unit (IMU) methods can reduce drift in joint motion estimation using rigid-body constraints, but they typically require pre-calibrated sensor locations, assumptions of specific joint models (e.g., hinge joints) or limited motion types, or an external reference for global orientation. Furthermore, challenges related to high-frequency impacts (e.g. at foot strikes during gait) and numerical differentiation of noisy measurements have not been explicitly addressed. We propose an extended Kalman filter that estimates relative IMU orientation and sensor location simultaneously, requiring neither offline sensor location calibration nor a global orientation anchor. A systematic simulation study shows that the quality of the relative orientation and the sensor location estimates depends on different motion-intensity sources: the joint center acceleration and the relative angular velocity, respectively. A low-pass filter (LPF) is used to mitigate real-signal outliers before applying the rigid-body constraint. When applied to the OpenSense dataset with real IMU measurements across four subjects with three lower-body joint types during walking and complex tasks including various motion types, the proposed method achieves an overall relative orientation error of 2.61°, compared with 10.40° for a baseline using offline-estimated sensor locations and 7.43° for a baseline using marker-derived sensor locations. These results show that the proposed calibration-free estimation framework handles real IMU data effectively and achieves accurate relative orientation without offline sensor location calibration.

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.