Reliable Autonomous Navigation in Semi - Structured Environments using the Three - Dimensional Normal Distributions Transform ( 3 D - NDT )

This thesis is concerned with autonomous navigation of a robotic vehicle in a semi-structured environment. Starting from the low-level problem of range sensing and moving on to more complex tasks like spatial representation, registration and mapping, path planning and following, this dissertation aims at providing a consistent picture of an autonomous navigation system. A unifying concept throughout this work is the use of the Three-Dimensional Normal Distributions Transform (3D-NDT) as a base for modeling space. The 3D-NDT is a compact, yet expressive means of modeling the environment, using a set of Gaussian probability density functions. Closely related to Gaussian Mixture Models (GMMs), the 3D-NDT can adequately model semi-structured indoor or natural environments. Unlike GMMs however, the 3D-NDT can be constructed very rapidly from 3D sensor data, making it ideal for use in timecritical algorithms, often necessary for successful autonomous navigation. One of the contributions of this dissertation is a method for comparison of spatial representation models. The proposed method is used on a large benchmarking data set, demonstrating the accuracy and expressive power of the 3DNDT. The proposed methodology is modified and used to compare the accuracy of four 3D range sensors in complex operational scenarios. A central task in robotics is that of registering range measurements in a consistent model of the environment. In an important contribution, we propose a registration algorithm which operates directly on the 3D-NDT representations of the raw point cloud data. Using the compactness and expressive power of the 3D-NDT, accurate registration results are obtained at run times, an order of magnitude faster then those of current algorithms. Algorithms to choose a good starting position for registration and to estimate the match covariance are also presented, paving the way to future 3D-NDT-based simultaneous localization and mapping (SLAM) solutions. This dissertation also makes a contribution to another essential component of an autonomous navigation system — namely the planning and safe execution of a drivable path. A traversability analysis technique, based on the 3D-NDT, is proposed and used in a modified wavefront path planner to produce safe paths in complex 3D environments. Finally, the proposed traversability analysis algorithm is used for obstacle detection and avoidance.