LiDAR systems estimate distances based on the transmission and reception times of laser beams, generating object representations in the form of point clouds. This research aims to develop a cost-efficient 3D mapping system by integrating LiDAR, GNSS, and camera sensors within a Mobile Mapping Vehicle (MMV) platform, where the sensors are mounted on a moving vehicle to enable continuous data acquisition. The main novelty of this research lies in the development of a low-cost multisensor fusion framework that integrates high-accuracy LiDAR-based geometric mapping with RGB-based 3D camera data. This approach not only produces precise object shape reconstructions but also enables automatic object type identification using the You Only Look Once (YOLO) artificial intelligence method. This approach overcomes the limitations of conventional LiDAR data, which lack adequate visual information to distinguish between object categories. The experimental results demonstrated that the LiDAR measurements yielded a maximum difference of 0.332 m and a minimum difference of 4.9 × 10 − 5 m compared with the ground truth, indicating good geometric accuracy consistent with Level of Detail (LOD) 2 quality. However, LiDAR data alone remain limited in identifying object types owing to the absence of RGB information. To address this limitation, 3D camera model data were integrated, providing enhanced visualization and enabling the effective classification of residential and commercial objects.
Triawan et al. (Fri,) studied this question.