This paper presents a new flying sensor system to capture 3D data aerially. The hardware system, consisting of a omni-directional laser scanner and a panoramic camera, can be mounted under a mobile platform (e.g., a balloon or a crane) to achieve the aerial scanning with high resolution and accuracy. Since the laser scanner often requires several minutes to complete an omni-directional scan, the raw data is distorted seriously due to the unknown and uncontrollable movement during the scanning period. To overcome this problem, 1) we first synchronize the two sensors and spherically calibrate them together, 2) our approach then recovers the sensor motion by utilizing the spacial and temporal features extracted both from the image sequences and point clouds, and 3) finally the distorted scans can be rectified with the estimated motion and aligned together automatically. In experiments, we demonstrate that the method achieves a substantially good performance for indoor/outdoor aerial scanning in the applications such as Angkor Wat 3D preservation and manufacturing 3D survey with respect to other state-of-the-art methods.

Laser range sensors are often demanded to mount on a moving platform for achieving the good efficiency of 3D reconstruction. However, such moving systems often suffer from the difficulty of matching the distorted range scans. In this paper, we propose novel 3D features which can be robustly extracted and matched even for the distorted 3D surface captured by a moving system. Our feature extraction employs Morse theory to construct Morse functions which capture the critical points approximately invariant to the 3D surface distortion. Then for each critical point, we extract support regions with the maximally stable region defined by extremal region or disconnectivity. Our feature description is designed as two steps: 1) we normalize the detected local regions to canonical shapes for robust matching, 2) we encode each key point with multiple vectors at different Morse function values. In experiments, we demonstrate that the proposed 3D features achieve substantially better performance for distorted surface matching than the state-of-the-art methods.