Correspondences between parts of shapes with particle filters

Given two shapes, the correspondence between distinct visual features is the basis for most alignment processes and shape similarity measures. This paper presents an approach introducing particle filters to establish perceptually correct correspondences between point sets representing shapes. Local shape feature descriptors are used to establish correspondence probabilities. The global correspondence structure is calculated using additional constraints based on domain knowledge. Domain knowledge is characterized as prior distributions expressing hypotheses about the global relationships between shapes. These hypotheses are generated during the iterative particle filtering process. Experiments using standard alignment techniques, based on the given correspondence relationships, demonstrate the advantages of this approach. 1. Introduction and Relate

Incremental multi-robot mapping

Abstract — The purpose of this paper is to present a technique to create a global map of robots ’ surroundings by converting the raw data acquired from a scanning sensor to a compact map composed of just a few generalized polylines (polygonal curves). We propose a new approach to merging robots ’ maps that is composed of a local geometric process of merging similar line segments (termed Discrete Segment Evolution) with a global statistical control process. In the case of single robot, we are able to incrementally build a map showing the environment the robot has traveled through by merging its polygonal map with actual scans. In the case of a robot team, we are able to identify common parts of their partial maps and if common parts are present construct a joint map of the explored environment

Quantitative Assessment of Robot-generated Maps

Mobile robotic mapping is now considered to be a sufficiently mature field with demonstrated successes in various domains. While much progress has been made in the development of computationally efficient and consistent mapping schemes, it is still murky, at best, on how these maps can be evaluated. We are motivated by the absence of an accepted standard for quantitatively measuring the performance of robotic mapping systems against user-defined requirements. It is our belief that the development of standardized methods for quantitatively evaluating existing robotic technologies will improve the utility of mobile robots in already established application areas, such as vacuum cleaning, robot surveillance, and bomb disposal. This approach will also enable the proliferation and acceptance of such technologies in emerging markets. This chapter summarizes our preliminary efforts by bringing together the research community towards addressing this important problem which has ramifications not only from researchers perspective but also from consumers, robot manufacturers, and developers viewpoints