For many centuries, philosophers and scientists have pondered the origins and nature of human intuitions about the properties of points, lines, and figures on the Euclidean plane, with most hypothesizing that a system of Euclidean concepts either is innate or is assembled by general learning processes. Recent research from cognitive and developmental psychology, cognitive anthropology, animal cognition, and cognitive neuroscience suggests a different view. Knowledge of geometry may be founded on at least two distinct, evolutionarily ancient, core cognitive systems for representing the shapes of large-scale, navigable surface layouts and of small-scale, movable forms and objects. Each of these systems applies to some but not all perceptible arrays and captures some but not all of the three fundamental Euclidean relationships of distance (or length), angle, and direction (or sense). Like natural number (Carey, 2009), Euclidean geometry may be constructed through the productive combination of representations from these core systems, through the use of uniquely human symbolic systems.

Abstract. When a purely rotating camera observes a general scene, overlapping views are related by a parallax-free warp which can be estimated by direct image alignment methods that iterate to optimise photoconsistency. However, building globally consistent mosaics from video has usually been tackled as an off-line task, while sequential methods suitable for real-time implementation have often suffered from long-term drift. In this paper we present a high performance real-time video mosaicing algorithm based on parallel image alignment via ESM (Efficient Second-order Minimisation) and global optimisation of a map of keyframes over the whole viewsphere. We present real-time results for drift-free camera rotation tracking and globally consistent spherical mosaicing from a variety of cameras in real scenes, demonstrating high global accuracy and the ability to track very rapid rotation while maintaining solid 30Hz operation. We also show that automatic camera calibration refinement can be straightforwardly built into our framework.

Abstract — One of the fundamental requirements of an autonomous vehicle is the ability to determine its location on a map. Frequently, solutions to this localization problem rely on GPS information or use expensive three dimensional (3D) sensors. In this paper, we describe a method for long-term vehicle localization based on visual features alone. Our approach utilizes a combination of topological and metric mapping, which we call topometric localization, to encode the coarse topology of the route as well as detailed metric information required for accurate localization. A topometric map is created by driving the route once and recording a database of visual features. The vehicle then localizes by matching features to this database at runtime. Since individual feature matches are unreliable, we employ a discrete Bayes filter to estimate the most likely vehicle position using evidence from a sequence of images along the route. We illustrate the approach using an 8.8 km route through an urban and suburban environment. The method achieves an average localization error of 2.7 m over this route, with isolated worst case errors on the order of 10 m. I.

journal homepage: www.elsevier.com/locate/cogpsych

Abstract — This article presents a solution to the problem of fusing measurements acquired from a monocular camera with inertial data to achieve simultaneous localization and mapping (SLAM) tasks. This paper describes the models used to correctly integrate inertial and vision data in an EKF-SLAM based application, and ways to perform the fusion on low cost hardware. Both synthetic and real sequences show that our method work and greatly enhance classical SLAM estimation. I.

journal homepage: www.elsevier.com/locate/cogpsych

c t i v it y e p o r t 2007 Table of contents

Abstract — Autonomous vehicles must be capable of localizing even in GPS denied situations. In this paper, we propose a realtime method to localize a vehicle along a route using visual imagery or range information. Our approach is an implementation of topometric localization, which combines the robustness of topological localization with the geometric accuracy of metric methods. We construct a map by navigating the route using a GPS-equipped vehicle and building a compact database of simple visual and 3D features. We then localize using a Bayesian filter to match sequences of visual or range measurements to the database. The algorithm is reliable across wide environmental changes, including lighting differences, seasonal variations, and occlusions, achieving an average localization accuracy of 1 m over an 8 km route. The method converges correctly even with wrong initial position estimates solving the kidnapped robot problem. I.

Abstract — This article presents a solution to the problem of fusing measurements acquired from a monocular camera with inertial data to achieve simultaneous localization and mapping (SLAM) tasks. This paper describes the models used to correctly integrate inertial and vision data in an EKF-SLAM based application, and ways to perform the fusion on low cost hardware. Both synthetic and real sequences show that our method work and greatly enhance classical SLAM estimation. I.