Visual topometric localization

Abstract

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.

Real-time topometric localization

“…In this paper, we develop a long-term localization algorithm that builds on the visual topometric localization approach proposed in [4]. Most existing visual localization approaches fall into two categories: metric or topological.…”

Understanding how camera configuration and environmental conditions affect appearance-based localization

“…For completeness, we briefly review the algorithm and its salient characteristics. Details of the algorithm are found in [5] and [6]. Metric localization, such as those based on SLAM [2] and visual odometry [4] are accurate for short distances, but they drift over time or fail at long sequences, especially if the vehicle does not revisit the same place on its trajectory.…”

“…On the other hand, topological localization methods, such as [23] estimate the observer's location qualitatively from a finite set of possible positions avoiding localization drift but provide only rough position estimates. Topometric localization was first introduced in [5] and improved in [6] as a combination of topological and metric localization. In topometric localization, the space of possible locations is discretized and modeled by a graph.…”

“…Although visual localization has been demonstrated in a variety of algorithms and systems [1], [5], [6], [12], [19] the problem of how to best configure such a system remains largely an open question. When developing a visual localization algorithm, a designer is faced with a number of trade-offs in terms of choice of camera, number of cameras, configuration of optics, sensor orientation, and frame rate.…”

“…By studying these effects, we can not only better understand how to configure a visual localization system, but also gain understanding of the underlying reasons that some configurations perform better than others. We will ground our investigation using extensive field testing of a visual topometric localization algorithm [5]. The conclusions, however, could be generalized to other algorithms that employ visual localization [8], [14].…”