iKalibr-RGBD: Partially-Specialized Target-Free Visual-Inertial Spatiotemporal Calibration For RGBDs via Continuous-Time Velocity Estimation

TL;DR

This paper introduces iKalibr-RGBD, a computationally efficient, targetless method for RGBD-inertial calibration using continuous-time ego-velocity estimation, improving over previous pose-based approaches.

Contribution

It presents a novel RGBD-inertial calibration method that replaces pose estimation with ego-velocity estimation for efficiency, inheriting the pipeline from iKalibr.

Findings

Achieves targetless calibration without artificial targets.

Reduces computational cost compared to pose-based methods.

Open-sourced implementation available for research use.

Abstract

Visual-inertial systems have been widely studied and applied in the last two decades (from the early 2000s 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 a prerequisite 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 target brings considerable convenience, computationally expensive pose estimation is demanded in initialization and batch optimization, limiting its availability. Fortunately, this could 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 paper, we present the continuous-time ego-velocity estimation-based RGBD-inertial spatiotemporal calibration, termed as iKalibr-RGBD, which is also targetless but computationally efficient. The general pipeline of iKalibr-RGBD is inherited from iKalibr, composed of a rigorous initialization procedure and several continuous-time batch optimizations. The implementation of iKalibr-RGBD is open-sourced at (https://github.com/Unsigned-Long/iKalibr) to benefit the research community.