Ground Plane-Aided Extrinsic Calibration of Inertial and RGB-D Sensors for Uncrewed Aerial Vehicles

2026-06-29

AI Summary

This work addresses the limitations of conventional extrinsic calibration methods between inertial sensors and RGB-D cameras on unmanned aerial vehicles, which typically rely on calibration targets, specialized equipment, and initial parameter estimates. To overcome these constraints, the authors propose a target-free self-calibration approach that uniquely integrates deep learning-based ground segmentation with geometric constraints. By segmenting ground regions from depth data, estimating point cloud normals, and aligning them with the gravity direction derived from IMU measurements, the method achieves robust extrinsic calibration without requiring any initial guess. Experimental results demonstrate that the proposed technique attains calibration accuracy surpassing the MATLAB Camera Calibrator Toolbox and comparable to Kalibr, while entirely eliminating dependence on calibration targets—thereby significantly enhancing deployment flexibility and practical applicability in real-world scenarios.

Abstract

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.

Problem

• extrinsic calibration
• inertial sensors
• RGB-D cameras
• Uncrewed Aerial Vehicles
• targetless calibration

Innovation

• targetless calibration
• ground plane segmentation
• extrinsic calibration
• IMU-RGBD fusion
• deep learning