Feature Based Occupancy Grid Maps for Sonar Based Safe-Mapping
Amit Kumar Pandey akpandey@research.iiit.ac.in K Madhava Krishna mkrishna@iiit.ac.in Mainak Nath mainak@students.iiit.ac.in

International Institute of Information Technology Robotics Research Center Gachibowli, Hyderabad (A.P.), India - 500032 Abstract
This paper presents a methodology for integrating features within the occupancy grid (OG) framework. The OG maps provide a dense representation of the environment. In particular they give information for every range measurement projected onto a grid. However independence assumptions between cells during updates as well as not considering sonar models lead to inconsistent maps, which may also lead the robot to take some decisions which may be unsafe or which may introduce an unnecessary overhead of run-time collision avoidance behaviors. Feature based maps provide more consistent representation by implicitly considering correlation between cells. But they are sparse due to sparseness of features in a typical environment. This paper provides a method for integrating feature based representations within the standard Bayesian framework of OG and provides a dense, more accurate and safe representation than standard OG methods. There have been two broad methods of overcoming these problems. Within the grid-based mapping paradigm, Howard and others [Howard and Kitchen, 1996] introduced the notion of response grids to alleviate some of the problems due to sonar characteristics. Here a grid was updated with a response value if it gave a response in atleast one of the n considered directions. In [Thrun 2003] a method of finding maximal likelihood maps based on forward sensor models was presented. Other authors bypass the OG framework to represent sonar data in terms of features. Some of the prominent methods include [Leonard and Whyte 1992]. In these methods features in the workspace such as edges and corners are identified from a circular scan of the...