A Comprehensive Uncertainty Analysis and Method of Geometric Calibration for a Circular Scanning Airborne Lidar

This dissertation describes an automated technique for ascertaining the values of the geometric calibration parameters of an airborne lidar. A least squares approach is employed that adjusts the point cloud to a single planar surface which could be either a narrow airport runway or a dynamic sea surface. Going beyond the customary three boresight angles, the proposed adjustment can determine up to eleven calibration parameters to a precision that renders a negligible contribution to the point cloud’s positional uncertainty. Presently under development is the Coastal Zone Mapping and Imaging Lidar (CZMIL), which, unlike most contemporary systems that use oscillating mirrors to reflect the beam, will use a circular spinning prism to refract the laser in the desired direction. This departure from the traditional scanner presents the potential for internal geometric misalignments not previously experienced. Rather than relying on past calibration practices (like requiring data be acquired over a pitched-roof), a more robust method of calibration is established which does not depend on the presence of any cultural features. To develop this new method of calibration, the laser point positioning equation for this lidar was developed first. The system was then simulated in the MATLAB environment. Using these artificial datasets, the behavior of each geometric parameter was systematically manipulated, understood and calibrated, while an optimal flight strategy for the calibration acquisition was simultaneously developed. Finally, the total propagated uncertainty (TPU) of the point cloud was determined using a propagation of variances. Using this TPU module, the strength of the calibration solution was assessed. For example, four flight lines each of 20 seconds in duration contained sufficient information to determine the calibration parameters to such a degree of confidence that their contribution to the final point cloud uncertainty was only 0.012m in the horizontal and 0.002m in the vertical (1σ)

GNSS/LiDAR-Based Navigation of an Aerial Robot in Sparse Forests

Autonomous navigation of unmanned vehicles in forests is a challenging task. In such environments, due to the canopies of the trees, information from Global Navigation Satellite Systems (GNSS) can be degraded or even unavailable. Also, because of the large number of obstacles, a previous detailed map of the environment is not practical. In this paper, we solve the complete navigation problem of an aerial robot in a sparse forest, where there is enough space for the flight and the GNSS signals can be sporadically detected. For localization, we propose a state estimator that merges information from GNSS, Attitude and Heading Reference Systems (AHRS), and odometry based on Light Detection and Ranging (LiDAR) sensors. In our LiDAR-based odometry solution, the trunks of the trees are used in a feature-based scan matching algorithm to estimate the relative movement of the vehicle. Our method employs a robust adaptive fusion algorithm based on the unscented Kalman filter. For motion control, we adopt a strategy that integrates a vector field, used to impose the main direction of the movement for the robot, with an optimal probabilistic planner, which is responsible for obstacle avoidance. Experiments with a quadrotor equipped with a planar LiDAR in an actual forest environment is used to illustrate the effectiveness of our approach

Self consistent bathymetric mapping from robotic vehicles in the deep ocean

Submitted In partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and Woods Hole Oceanographic Institution June 2005Obtaining accurate and repeatable navigation for robotic vehicles in the deep ocean is difficult and consequently a limiting factor when constructing vehicle-based bathymetric maps. This thesis presents a methodology to produce self-consistent maps and simultaneously improve vehicle position estimation by exploiting accurate local navigation and utilizing terrain relative measurements. It is common for errors in the vehicle position estimate to far exceed the errors associated with the acoustic range sensor. This disparity creates inconsistency when an area is imaged multiple times and causes artifacts that distort map integrity. Our technique utilizes small terrain "submaps" that can be pairwise registered and used to additionally constrain the vehicle position estimates in accordance with actual bottom topography. A delayed state Kalman filter is used to incorporate these sub-map registrations as relative position measurements between previously visited vehicle locations. The archiving of previous positions in a filter state vector allows for continual adjustment of the sub-map locations. The terrain registration is accomplished using a two dimensional correlation and a six degree of freedom point cloud alignment method tailored for bathymetric data. The complete bathymetric map is then created from the union of all sub-maps that have been aligned in a consistent manner. Experimental results from the fully automated processing of a multibeam survey over the TAG hydrothermal structure at the Mid-Atlantic ridge are presented to validate the proposed method.This work was funded by the CenSSIS ERC of the Nation Science Foundation under grant EEC-9986821 and in part by the Woods Hole Oceanographic Institution through a grant from the Penzance Foundation

Roving vehicle motion control Final report

Roving vehicle motion control for unmanned planetary and lunar exploratio

Road Surface Feature Extraction and Reconstruction of Laser Point Clouds for Urban Environment

Automakers are developing end-to-end three-dimensional (3D) mapping system for Advanced Driver Assistance Systems (ADAS) and autonomous vehicles (AVs). Using geomatics, artificial intelligence, and SLAM (Simultaneous Localization and Mapping) systems to handle all stages of map creation, sensor calibration and alignment. It is crucial to have a system highly accurate and efficient as it is an essential part of vehicle controls. Such mapping requires significant resources to acquire geographic information (GIS and GPS), optical laser and radar spectroscopy, Lidar, and 3D modeling applications in order to extract roadway features (e.g., lane markings, traffic signs, road-edges) detailed enough to construct a “base map”. To keep this map current, it is necessary to update changes due to occurring events such as construction changes, traffic patterns, or growth of vegetation. The information of the road play a very important factor in road traffic safety and it is essential for for guiding autonomous vehicles (AVs), and prediction of upcoming road situations within AVs. The data size of the map is extensive due to the level of information provided with different sensor modalities for that reason a data optimization and extraction from three-dimensional (3D) mobile laser scanning (MLS) point clouds is presented in this thesis. The research shows the proposed hybrid filter configuration together with the dynamic developed mechanism provides significant reduction of the point cloud data with reduced computational or size constraints. The results obtained in this work are proven by a real-world system

SIMLIDAR Simulation of LiDAR performance in artificially simulate orchards

SIMLIDAR is an application developed in Cþþ that generates an artificial orchard using a Lindenmayer system. The application simulates the lateral interaction between the artificial orchard and a laser scanner or LIDAR (Light Detection and Ranging). To best highlight the unique qualities of the LIDAR simulation, this work focuses on apple trees without leaves, i.e. the woody structure. The objective is to simulate a terrestrial laser sensor (LIDAR) when applied to different artificially created orchards and compare the simulated characteristics of trees with the parameters obtained with the LIDAR. The scanner is mounted on a virtual tractor and measures the distance between the origin of the laser beam and the nearby plant object. This measurement is taken with an angular scan in a plane which is perpendicular to the route of the virtual tractor. SIMLIDAR determines the distance measured in a bi-dimensional matrix N M, where N is the number of angular scans and M is the number of steps in the tractor route. In order to test the data and performance of SIMLIDAR, the simulation has been applied to 42 different artificial orchards. After previously defining and calculating two vegetative parameters (wood area and wood projected area) of the simulated trees, a good correlation (R2 ¼ 0.70e0.80) was found between these characteristics and the wood area detected (impacted) by the laser beam. The designed software can be valuable in horticulture for estimating biomass and optimising the pesticide treatments that are performed in winter

Presenting an Automated Calibration Procedure for an Airborne Lidar System and its Potential Application to Acoustic Hydrography

Whether using an airborne lidar or a ship-based acoustic system, all hydrographers must contend with geometric system calibrations. A poorly aligned system leads to erroneously reported depths, diminished system resolution and internally inconsistent datasets. Most of today’s calibration procedures are cumbersome and subjective enterprises that possess little statistical merit. This paper presents a least squares adjustment algorithm designed to calibrate a (presently under-development) lidar. This method is automated, objective, repeatable, and reports a confidence on the calibration values. Using simulated lidar datasets, the algorithm is explained and demonstrated. A brief modification is also proposed to expand the use to multibeam echosounders.Independientemente de si se usa un lidar aerotransportado o un sistema acústico embarcado, todos los hidrógrafos deben enfrentarse a las calibraciones de sistemas geométricos. Un sistema escasamente alineado conduce a errores en las profundidades indicadas, a disminución de la resolución del sistema y a colecciones de datos internamente inconsistentes. La mayoría de los procedimientos de calibración actuales son complicados y sujetos a tareas que poseen poco mérito estadístico. Este artículo presenta un algoritmo de ajuste mediante un método de mínimos cuadrados designado para calibrar un lidar (en vías de desarrollo actualmente). Este método es automatizado, objetivo, repetible, e indica una confianza en los valores de calibración. El algoritmo se explica y se demuestra utilizando colecciones de datos del lidar simulado. Se propone también una breve modificación para ampliar el uso a los sondadores acústicos multihaz.Que ce soit à l’aide d’un lidar aéroporté ou d’un système acoustique embarqué, tous les hydrographes doivent faire face à des étalonnages de systèmes géométriques. Un système mal aligné conduit à des erreurs dans les profondeurs indiquées, à une diminution de la résolution du système et à des ensembles de données inconsistants en interne. La plupart des procédures d’étalonnage actuelles sont compliquées et sujettes à des tâches qui n’ont qu’un faible mérite statistique. Cet article présente un algorithme d’ajustement à l’aide de la méthode des moindres carrés conçu pour étalonner un système lidar (actuellement en développement). Cette méthode est automatique, objective, répétable et rend compte d’une confiance dans les valeurs d’étalonnage. A l’aide d’ensembles de données lidar simulées, l’algorithme est expliqué et démontré. Une brève modification est également proposée afin d’étendre leur utilisation aux échosondeurs multifaisceaux