Over the last few years, LiDAR has become a popular technology for the direct acquisition of topographic information. In spite of the increasing utilization of this technology in several applications, its accuracy potential has not been fully explored. Most of current LiDAR calibration techniques are based on empirical and proprietary procedures that demand the system’s raw measurements, which may not be always available to the end-user. As a result, we can still observe systematic discrepancies between conjugate surface elements in overlapping LiDAR strips. In this paper, two alternative calibration procedures that overcome the existing limitations are introduced. The first procedure, denoted as “Simplified method”, makes use of the LiDAR point cloud from parallel LiDAR strips acquired by a steady platform (e.g., fixed wing aircraft) over an area with moderately varying elevation. The second procedure, denoted as “Quasi-rigorous method”, can deal with non-parallel strips, but requires time-tagged LiDAR point cloud and navigation data (trajectory position only) acquired by a steady platform. With the widespread adoption of LAS format and easy access to trajectory information, this data requirement is not a problem. The proposed methods can be applied in any type of terrain coverage without the need for control surfaces and are relatively easy to implement. Therefore, they can be used in every flight mission if needed. Besides, the proposed procedures require minimal interaction from the user, which can be completely eliminated after minor extension of the suggested procedure.

The Kinect system is arguably the most popular 3-D camera technology currently on the market. Its application domain is vast and has been deployed in scenarios where accurate geometric measurements are needed. Regarding the PrimeSense technology, a limited amount of work has been devoted to calibrating the Kinect, especially the depth data. The Kinect is, however, inevitably prone to distortions, as independently confirmed by numerous users. An effective method for improving the quality of the Kinect system is by modeling the sensor's systematic errors using bundle adjustment. In this paper, a method for modeling the intrinsic and extrinsic parameters of the infrared and colour cameras, and more importantly the distortions in the depth image, is presented. Through an integrated marker-and feature-based self-calibration, two Kinects were calibrated. A novel approach for modeling the depth systematic errors as a function of lens distortion and relative orientation parameters is shown to be effective. The results show improvements in geometric accuracy up to 53% compared with uncalibrated point clouds captured using the popular software RGBDemo. Systematic depth discontinuities were also reduced and in the check-plane analysis the noise of the Kinect point cloud was reduced by 17%.

Autonomous Simultaneous Localization and Mapping (SLAM) is an important topic in many engineering fields. Since stop-and-go systems are typically slow and full-kinematic systems may lack accuracy and integrity, this paper presents a novel hybrid “continuous stop-and-go” mobile mapping system called Scannect. A 3D terrestrial LiDAR system is integrated with a MEMS IMU and two Microsoft Kinect sensors to map indoor urban environments. The Kinects’ depth maps were processed using a new point-to-plane ICP that minimizes the reprojection error of the infrared camera and projector pair in an implicit iterative extended Kalman filter (IEKF). A new formulation of the 5-point visual odometry method is tightly coupled in the implicit IEKF without increasing the dimensions of the state space. The Scannect can map and navigate in areas with textureless walls and provides an effective means for mapping large areas with lots of occlusions. Mapping long corridors (total travel distance of 120 m) took approximately 30 minutes and achieved a Mean Radial Spherical Error of 17 cm before smoothing or global optimization.

Terrestrial laser scanners are sophisticated instruments that operate much like high-speed total stations. It has previously been shown that unmodelled systematic errors can exist in modern terrestrial laser scanners that deteriorate their geometric measurement precision and accuracy. Typically, signalised targets are used in point-based self-calibrations to identify and model the systematic errors. Although this method has proven its effectiveness, a large quantity of signalised targets is required and is therefore labour-intensive and limits its practicality. In recent years, feature-based self-calibration of aerial, mobile terrestrial, and static terrestrial laser scanning systems has been demonstrated. In this paper, the commonalities and differences between point-based and plane-based self-calibration (in terms of model identification and parameter correlation) are explored. The results of this research indicate that much of the knowledge from point-based self-calibration can be directly transferred to plane-based calibration and that the two calibration approaches are nearly equivalent. New network configurations, such as the inclusion of tilted scans, were also studied and prove to be an effective means for strengthening the self-calibration solution, and improved recoverability of the horizontal collimation axis error for hybrid scanners, which has always posed a challenge in the past.