This paper presents an experimental evaluation of different line extraction algorithms on 2D laser scans for indoor environment. Six popular algorithms in mobile robotics and computer vision are selected and tested. Experiments are performed on 100 real data scans collected in an office environment with a map size of 80m × 50m. Several comparison criteria are proposed and discussed to highlight the advantages and drawbacks of each algorithm, including speed, complexity, correctness and precision. The results of the algorithms are compared with the ground truth using standard statistical methods.

The Spatial Semantic Hierarchy (SSH) is a set of distinct representations for large scale space, each with its own ontology and each abstracted from the levels below it. At the control level, the agent and its environment are modeled as continuous dynamical systems whose equilibrium points are abstracted to a discrete set of distinctive states. The control laws whose execution defines trajectories linking these states are abstracted to actions, giving a discrete causal graph representation for the state space. The causal graph of states and actions is in turn abstracted to a topological network of places and paths (i.e. the topological map). Local metrical models of places and paths can be built within the framework of the control, causal and topological levels while avoiding problems of global consistency. ...

Abstract — Today, lightweight SLAM algorithms are needed in many embedded robotic systems. In this paper the Orthogonal SLAM (OrthoSLAM) algorithm is presented and empirically validated. The algorithm has constant time complexity in the state estimation and is capable to run real-time. The main contribution resides in the idea of reducing the complexity by means of an assumption on the environment. This is done by mapping only lines that are parallel or perpendicular to each other which represent the main structure of most indoor environments. The combination of this assumption with a Kalman Filter and a Relative Map approach is able to map our laboratory hallway with the size of 80m × 50m and a trajectory of more than 500m. The precision of the resulting map is similar to the measurements done by hand which are used as the ground-truth.

This paper presents some techniques for sensing and perception for an omnidirectional ground autonomous vehicle equipped with a laser scanner. In an assumed structured environment, the sensor data processing methods for both 1D and 2D laser scanners, are discussed. Raw data are segmented to lines, circles, ellipses, planes and corners by task dependent segmentation algorithms. Each subset of data is then fit by known template shapes as listed above. An arbitrator is used to discriminate the shape for the final object identification.

This paper explores the fusion of inertial information with vision for 3D reconstruction. A method is proposed for vertical line segment detection and subsequent local geometric map building. Visual and inertial sensing are two sensory modalities that can be explored to give robust solutions on image segmentation and recovery of 3D structure from images, increasing the capabilities of autonomous vehicles and enlarging the application potential of vision systems. From the inertial sensors, a camera stereo rig, and a few system parameters we can recover the 3D parameters of the ground plane and vertical lines. The homography between stereo images of ground points can be found. By detecting the vertical line segments in each image, and using the homography of ground points for the foot of each segment, the lines can be matched and reconstructed in 3D. The mobile robot then maps the detected vertical line segments in a world map as it moves. To build this map an outlier removal method is implemented and a statistical approach used, so that a simplified metric map can be obtained for robot navigation.

In this paper, a method is proposed for building occupancy grid based maps from sequences of stereo images in real time. The stereo camera can move and rotate unconstrained in three dimensions. This distinguishes the method from all other mapping approaches, which constrain the sensor to move in two dimensions. This freedom of motion allows to perform mapping in many new application areas. Additionally, three dimensional information can be maintained in layers or fused into a two dimensional overview of the environment. This transition is based on changeable rules rather than constraints. The method uses fuzzy logic and eliminates many problems that arise from traditional, probabilistic approaches for occupancy grids. It is shown that the proposed method performs well, is memory efficient and suitable for incremental use in real-time. The map will be used to assist navigation of a tele-operated mobile robot and real-time virtual walkthroughs, which are generated from the same stereo and motion data.

This paper describes the new localisation algorithms under implementation for the mail distributing mobile robot, MOPS, of the Institute of Robotics, Swiss Federal Institute of Technology Zurich. Using geometric primitives as features, we employ consistent probabilistic feature extraction, clustering, matching and estimation of the vehicle position and orientation. The extracted features and their jirst-order covariance estimates are used, together with a world model, by an extended Kalman filter so as to get an optimal estimate of MOPS ’ current pose vector and the associated uncertainty. The line extraction consists of an initial segmentation, based on a feature-independent compactness measure in the model space, and a subsequent probabilistic clustering step. This yielh a highly accurate and eficient localisation. 1.

First, I would like to thank my family for their love and support during my time at the University of Florida. They have always believed in my potential and have never placed any undue expectations on me. I thank them for allowing me the freedom to pursue my dreams. I would also like to thank my advisors Dr. A. Antonio Arroyo and Dr. Carl Crane for their guidance and support over the years. It was only through their help that I was able to participate in the 2007 Urban Challenge and work on a myriad of interesting projects during my time at the Center for Intelligent Machines and Robotics. In addition, I would like to thank the other members of my committee, Dr. Douglas Dankel, Dr. Herman Lam, and Dr. Eric Schwartz for their valuable input and guidance over the past few