Mobile light detection and ranging (LiDAR) systems are widely used to generate precise 3-D spatial information, which in turn aids a variety of applications such as digital building model generation, transportation corridor asset management, telecommunications, precision agriculture, and infrastructure monitoring. Integrating such systems with one or more cameras would allow forward and backward projection between imagery and LiDAR data, thus facilitating several other high-level data processing activities, such as reliable feature extraction and colorization of point cloudsv. To increase the registration accuracy of point clouds derived from LiDAR data and imagery, along with their accuracy with respect to the ground truth, a simultaneous estimation of the mounting parameters relating the different laser scanners and cameras to the onboard global navigation satellite system (GNSS)/inertial navigation system (INS) unit is vital. This paper proposes a calibration procedure that directly estimates the mounting parameters for several spinning multibeam laser scanners and cameras onboard an airborne or terrestrial mobile platform. This procedure is based on the use of conjugate points and linear/planar features in overlapping images and point clouds derived from different drive-runs/flight lines. In order to increase the efficiency of semi-automatic conjugate feature extraction from the LiDAR data, specifically designed calibration boards covered by highly reflective surfaces that could be easily deployed and set up within an outdoor environment are used in this study. An experimental setup is used to evaluate the performance of the proposed calibration procedure for both airborne and terrestrial mobile mapping systems through the a posteriori variance factor of the least squares adjustment procedure and the quality of fit of the adjusted LiDAR point cloud and image points to linear/planar surfaces before and after the calibration process.

This paper proposes a multiunit light detection and ranging (LiDAR) system calibration procedure to directly estimate the mounting parameters relating multiple spinning multibeam laser scanners to the global navigation satellite system/inertial navigation system (GNSS/INS) unit onboard a mobile terrestrial platform in order to derive point clouds with high-positional accuracy. This procedure is based on the use of conjugate planar/linear features in overlapping point clouds derived from different drive runs. In order to increase the efficiency of semiautomatic conjugate feature extraction from LiDAR data, specifically designed calibration boards covered by highly reflective surfaces that could be easily deployed and set up within outdoor environments are used in this paper. To ensure the accuracy of the estimated mounting parameters, an optimal configuration of target primitives and drive runs is determined by analyzing the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud for different orientations of target primitives and different drive run scenarios. This impact is also verified experimentally by simulating a bias in each mounting parameter separately. Finally, the optimal configuration is used within an experimental setup to evaluate the performance of the proposed calibration procedure through the a posteriori variance factor of least squares adjustment and the quality of fit of adjusted point cloud to linear/planar surfaces before and after the calibration process. The proposed calibration approach attained an accuracy of 1.42 cm, which is better than the accuracy expected based on the specifications of the involved hardware components, i.e., the LiDAR and GNSS/INS units.

Light Detection and Ranging (LiDAR) is a technology that uses laser beams to measure ranges and generates precise 3D information about the scanned area. It is rapidly gaining popularity due to its contribution to a variety of applications such as Digital Building Model (DBM) generation, telecommunications, infrastructure monitoring, transportation corridor asset management and crash/accident scene reconstruction. To derive point clouds with high positional accuracy, estimation of mounting parameters relating the laser scanners to the onboard Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) unit, i.e., the lever-arm and boresight angles, is the foremost and necessary step. This paper proposes a LiDAR system calibration strategy for a Unmanned Aerial Vehicle (UAV)-based mobile mapping system that can directly estimate the mounting parameters for spinning multi-beam laser scanners through an outdoor calibration procedure. This approach is based on the use of conjugate planar/linear features in overlapping point clouds derived from different flight lines. Designing an optimal configuration for calibration is the first and foremost step in order to ensure the most accurate estimates of mounting parameters. This is achieved by conducting a rigorous theoretical analysis of the potential impact of bias in mounting parameters of a LiDAR unit on the resultant point cloud. The dependency of the impact on the orientation of target primitives and relative flight line configuration would help in deducing the configuration that would maximize as well as decouple the impact of bias in each mounting parameter so as to ensure their accurate estimation. Finally, the proposed analysis and calibration strategy are validated by calibrating a UAV-based LiDAR system using two different datasets—one acquired with flight lines at a single flying height and the other with flight lines at two different flying heights. The calibration performance is evaluated by analyzing correlation between the estimated system parameters, the a-posteriori variance factor of the Least Squares Adjustment (LSA) procedure and the quality of fit of the adjusted point cloud to planar/linear features before and after the calibration process.

The emergence of Mobile Mapping Systems (MMS) has set a marked paradigm in the photogrammetric and mapping community that has not only facilitated comprehensive 3D mapping of different environments but has also paved way for new aspects of applied research in this direction. Out of the many essential blocks that make these MMS a viable tool for mapping, the positioning and orientation module is considered to be a crucial yet an expensive component. The integration of such a module with mapping sensors has allowed for the extensive implementation of such systems to provide high-quality maps. However, while such systems do not lack in system robustness and performance in general, the deployment of these systems is restricted to applications and environments where a consistent availability of GNSS signals is assured. Extending these MMS to GNSS-denied areas, such as indoor environments, is therefore quite challenging and necessitates the development of an alternative module that can act as a viable substitute to GNSS/INS for system operation without having to resort to an exhaustive modification of the same to function in GNSS-denied locations. In this research, such a case has been considered for the implementation of an indoor MMS using an Unmanned Ground Vehicle (UGV) and a 3D laser scanner for the task of generating high density maps of GNSS-denied indoor areas. To mitigate the absence of GNSS data, this paper proposes a Pseudo-GNSS/INS module integrated framework which utilizes probabilistic Simultaneous Localization and Mapping (SLAM) techniques to estimate the platform pose and heading from 3D laser scanner data. This proposed framework has been implemented based on three major notions: (i) using geometric methods for sparse point cloud extraction to carry out real-time SLAM, (ii) generating position data and geo-referencing signals from these real-time SLAM pose estimates, and (iii) carrying out the entire operation through use of a single 3D mapping sensor. The final geo-referenced point cloud can then be generated through post-processing by the Iterative Closest Projected Point (ICPP) registration technique which also diminishes the effect of sensor measurement noise. The implementation, performance and results of the proposed MMS framework for an indoor mapping system have been presented in this paper that demonstrate the ability of this Pseudo-GNSS/INS framework to operate flexibly in GNSS-denied areas.

The increasing use of Unmanned Aerial Vehicles (UAVs) highlights the need for robust classification systems. This study explores the impact of noise on UAV classification accuracy using radar-based deep learning. A noise-free dataset of range-Doppler maps (RDMs) is generated from digital twin simulations, and noisy datasets are created by adding Additive White Gaussian Noise (AWGN) across Signal-to-Noise Ratio (SNR) levels from -20 dB to 10 dB. A Deep Learning (DL) model, trained on a merged dataset, achieves an accuracy exceeding 98%. Performance evaluation shows accuracy dropping to 34% at -14 dB SNR, with Receiver Operating Characteristic (ROC) curves used for analysis. Inference at the software level is conducted using TensorFlow on a PC within the Vitis-AI Docker environment, achieving an accuracy of 96.39%. The quantized model, when deployed on the KR260 Field-Programmable Gate Array (FPGA), attains an accuracy of 94.73%. Although there is a slight drop in accuracy, the hardware implementation demonstrates impressive performance, supporting a real-time UAV classification system that is effective across different noise levels. Finally, the pre-trained model is deployed on an FPGA processing platform to evaluate its effectiveness for real-time classification. By leveraging the FPGA’s real-time processing capabilities, this approach satisfies the stringent speed and accuracy requirements necessary for UAV classification.

Due to the advances in technological and industrial fields, remote sensing has been adopted to a considerable extent in precision agricultural applications. Over the past few years, remote sensing utilized Mobile Mapping Systems (MMS) as the platforms for agricultural data collection. For accurate generation of georeferenced products using such MMSs, there should be a robust calibration approach that can accurately estimate the mounting parameters of the involved sensors, i.e., LiDAR unit, camera, and hyperspectral push-broom scanner. In this paper, we propose novel calibration approaches for various sensors onboard a UAV platform - 1) simultaneous estimation of lever arm and boresight angles relating LiDAR unit and camera to the GNSS/INS unit, and 2) estimation of boresight angles relating hyperspectral push-broom scanner and the GNSS/TNS unit.