Unified Motion-Based Calibration of Mobile Multi-Sensor Platforms With Time Delay Estimation

Abstract

The ability to maintain and continuously update geometric calibration parameters of a mobile platform is a key functionality for every robotic system. These parameters include the intrinsic kinematic parameters of the platform, the extrinsic parameters of the sensors mounted on it and their time delays. In this paper, we present a unified pipeline for motionbased calibration of mobile platforms equipped with multiple heterogeneous sensors.We formulate a unified optimization problem to concurrently estimate the platform kinematic parameters, the sensors extrinsic parameters and their time delays. We analyze the influence of the trajectory followed by the robot on the accuracy of the estimate. Our framework automatically selects appropriate trajectories to maximize the information gathered and to obtain a more accurate parameters estimate. In combination with that, our pipeline observes the parameters evolution in long-term operation to detect possible values change in the parameters set. The experiments conducted on real data show a smooth convergence along with the ability to detect changes in parameters value.We release an open-source version of our framework to the community.

Automatic Multi-Sensor Extrinsic Calibration For Mobile Robots

“…Additionally, relying on known landmarks for calibration prevents the automatic execution of the calibration process since they have to be placed in the environment beforehand. However, automatic calibration methods are interesting for mobile robots: they demand re-calibration capabilities for long-term operation since the relative transformation between their sensing devices can change due to crashes, vibrations, human intervention, etc [8], [9]. In contrast to pattern-based calibration methods, motionbased ones do not need to modify the environment and they can be used with any sensing devices, provided that egomotion can be inferred from them.…”

“…We also reported the average and the absolute and relative Root Mean Squared Error (RMSE). We included the noise-free case (λ = 0) to show the correctness of our formulation and compared it with the method in [8]. The approach in [8] incorrectly detects a time delay and bias its solution.…”

“…We included the noise-free case (λ = 0) to show the correctness of our formulation and compared it with the method in [8]. The approach in [8] incorrectly detects a time delay and bias its solution. Additionally, it cannot handle higher levels of noise.…”

“…We chose such a trajectory as the synchronization time reference as it has the lower rate of the two and thus interpolated camera poses can be computed more accurately. Both the proposed method and the motion-based approach in [8] are given the same three input trajectories for the sake of fairness.…”

Intrinsic Calibration of Depth Cameras for Mobile Robots Using a Radial Laser Scanner

“…With the massive deployment of robotic platforms [16], calibration methods suitable to be executed automatically by robots are desirable, seeking to prevent the manual calibration of each depth sensor prior to deployment. However, existing intrinsic calibration methods for structured-light depth cameras cannot be easily automated.…”