Radiometric and Geometric Calibration of an Inexpensive LED-Based Lidar Sensor



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Radiometric calibration of traditional lidar sensors that employ direct time of flight or phase-based ranging is well established. However, emerging inexpensive, lightweight, short-range lidar sensors that utilize non-traditional ranging methods report measurements that are not appropriate for existing radiometric calibration techniques. One such sensor, the TeraRanger Evo 60m by Terabee is a light emitting diode (instead of laser) lidar sensor with an automatically varying collection rate. This thesis investigates the performance of a new radiometric calibration model, one based on a neural network, applied to the Evo 60m. Application of the proposed radiometric calibration model resulted in performance similar to traditional lidar sensors, with mean differences in reflectance of no more than 5% and root mean square errors of no more than 6% for non-specular targets. The radiometric calibration model provides a generic approach that may be applicable to other low-cost lidar sensors and is a potential stepping stone toward development of a low-cost, multiple wavelength (multispectral) lidar sensor. The ranging performance of the Evo 60m was also evaluated in this work. Three of the four sensors evaluated fall below the manufacturer’s stated accuracy level of ±40 millimeters while one lies just above the threshold at ±43 millimeters.



Lidar, Radiometric Calibration, Neural Networks, Machine Learning, Geometric Calibration