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Fusing data from LiDAR and camera is conceptually attractive because of theircomplementary properties. For instance, camera images are higher resolution andhave colors, while LiDAR data provide more accurate range measurements and havea wider Field Of View (FOV). However, the sensor fusion problem remainschallenging since it is difficult to find reliable correlations between data ofvery different characteristics (geometry vs. texture, sparse vs. dense). Thispaper proposes an offline LiDAR-camera fusion method to build dense, accurate3D models. Specifically, our method jointly solves a bundle adjustment (BA)problem and a cloud registration problem to compute camera poses and the sensorextrinsic calibration. In experiments, we show that our method can achieve anaveraged accuracy of 2.7mm and resolution of 70 points per square cm bycomparing to the ground truth data from a survey scanner. Furthermore, theextrinsic calibration result is discussed and shown to outperform thestate-of-the-art method.

A Joint Optimization Approach of LiDAR-Camera Fusion for Accurate Dense 3D Reconstructions