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Mobile laser scanning (MLS) point cloud registration plays a critical role in mobile 3D mapping and inspection, but conventional point cloud registration methods for terrain LiDAR scanning (TLS) are not suitable for MLS. To cope with this challenge, we use inertial measurement unit (IMU) to assist registration and propose an MLS point cloud registration method based on an inertial trajectory error model. First, we propose an error model of inertial trajectory over a short time period to construct the constraints between trajectory points at different times. On this basis, a relationship between the point cloud registration error and the inertial trajectory error is established, then trajectory error parameters are estimated by minimizing the point cloud registration error using the least squares optimization. Finally, a reliable and concise inertial-assisted MLS registration algorithm is realized. We carried out experiments in three different scenarios: indoor, outdoor and integrated indoor–outdoor. We evaluated the overall performance, accuracy and efficiency of the proposed method. Compared with the ICP method, the accuracy and speed of the proposed method were improved by 2 and 2.8 times, respectively, which verified the effectiveness and reliability of the proposed method. Furthermore, experimental results show the significance of our method in constructing a reliable and scalable mobile 3D mapping system suitable for complex scenes.

Fisheye cameras are widely used in visual localization due to the advantage of the wide field of view. However, the severe distortion in fisheye images lead to feature matching difficulties. This paper proposes an IMU-assisted fisheye image matching method called spherically optimized random sample consensus (So-RANSAC). We converted the putative correspondences into fisheye spherical coordinates and then used an inertial measurement unit (IMU) to provide relative rotation angles to assist fisheye image epipolar constraints and improve the accuracy of pose estimation and mismatch removal. To verify the performance of So-RANSAC, experiments were performed on fisheye images of urban drainage pipes and public data sets. The experimental results showed that So-RANSAC can effectively improve the mismatch removal accuracy, and its performance was superior to the commonly used fisheye image matching methods in various experimental scenarios.

In environments that are hostile to Global Navigation Satellites Systems (GNSS), the precision achieved by a mobile light detection and ranging (LiDAR) system (MLS) can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system (POS). This paper proposes a novel least squares collocation (LSC)-based method to improve the accuracy of the MLS in these hostile environments. Through a thorough consideration of the characteristics of POS errors, the proposed LSC-based method effectively corrects these errors using LiDAR control points, thereby improving the accuracy of the MLS. This method is also applied to the calibration of misalignment between the laser scanner and the POS. Several datasets from different scenarios have been adopted in order to evaluate the effectiveness of the proposed method. The results from experiments indicate that this method would represent a significant improvement in terms of the accuracy of the MLS in environments that are essentially hostile to GNSS and is also effective regarding the calibration of misalignment.