iKalibr-RGBD: Partially-Specialized Target-Free Visual-Inertial Spatiotemporal Calibration for RGBDs via Continuous-Time Velocity Estimation
Shuolong Chen, Xingxing Li, Shengyu Li, Yuxuan Zhou · IEEE Robotics and Automation Letters · 2024
Visual-inertial systems have been widely studied and applied in the last two decades (from the early 2000 s to the present), mainly due to their low cost and power consumption, small footprint, and high availability. Such a trend simultaneously leads to a large amount of visual-inertial calibration methods being presented, as accurate spatiotemporal parameters between sensors are essential for visual-inertial fusion. In our previous work, i.e.,iKalibr, a continuous-time-based visual-inertial calibration method was proposed as a part of one-shot multi-sensor resilient spatiotemporal calibration. While requiring no artificial calibration target brings considerable convenience, computationally expensive pose estimation is demanded in initialization and batch optimization, limiting its availability. Fortunately, this can be vastly improved for the RGBDs with additional depth information, by employing mapping-free ego-velocity estimation instead of mapping-based pose estimation. In this letter, we present an ego-velocity-estimation-based RGBD-inertial spatiotemporal calibrator, termed asiKalibr-RGBD, which is also targetless and continuous-time-based, but computationally efficient. The general pipeline ofiKalibr-RGBDis inherited fromiKalibr, composed of a rigorous initialization procedure and several continuous-time batch optimizations.