UCSR: Registration and Fusion of Cross-Source 2D and 3D Sensor Data in Unstructured Environments

Abstract

Data fusion in multi-sensor systems requires an accurate calibration of the sensors. To this end, the sensor data itself can be registered, which evades often inaccurate, manual measurements on the sensor setup. This work proposes a flexible registration framework for the calibration of multi-sensor systems in unstructured environments, denoted Unstructured Cross-Source Registration (UCSR). In unstructured environments, the registration of sensor data presents a major challenge due to the absence of structure, flat surfaces, and clearly separated objects. Cross-source data, captured with different types of sensors, presents differences in scale, measurement density, accuracy, noise, and outlier characteristics, which poses an additional challenge in registration. At present, three methods for the cross-source registration are included in UCSR. Each method is evaluated independently on real-world 2D and 3D data. To achieve a stable calibration, UCSR combines the registration results of the individual methods according to their accuracy and robustness. UCSR does neither require special calibration objects nor human intervention. Cross-source 2D and 3D sensor data is captured with a mobile robotic off-road platform. UCSR is able to register sensor data with a decalibration of 2.0 m along and 20° around each axis. UCSR achieves a mean accuracy of 5.1 cm and 0.956° on 2D and 3D data from unstructured environments.