Bayesian Learning of Occupancy Grids

Learning occupancy grids with forward models

This paper presents a new way to acquire occupancy grid maps with mobile robots. Virtually all existing occupancy grid mapping algorithms decompose the highdimensional mapping problem into a collection of onedimensional problems, where the occupancy of each grid cell is estimated independently of others. This induces conflicts that can lead to inconsistent maps. This paper shows how to solve th...

Learning Occupancy Grids With Forward Sensor Models

This article describes a new algorithm for acquiring occupancy grid maps with mobile robots. Virtually all existing occupancy grid mapping algorithms decompose the highdimensional mapping problem into a collection of one-dimensional problems, where the occupancy of each grid cell is estimated independently. This induces conflicts that may lead to inconsistent maps. This article shows how to sol...

Learning Occupancy Grids of Non-Stationary Objects with Mobile Robots

We propose an occupancy grid mapping algorithm for mobile robots operating in environments where objects may change their locations over time. Most mapping algorithms rely on a static world assumption, which cannot model non-stationary objects (chairs, desks, . . . ). This paper describes an extension to the well-known occupancy grid mapping technique [5,10] for learning models of non-stationar...

Confidence-aware Occupancy Grids

Representation of Odometry Errors on Occupancy Grids

In this work we propose an enhanced model for mapping from sonar sensors and odometry that allows a robot to represent an environment map in a more suitable way to both the sonar sensory data and odometry system of the robot. We use a stochastic modelling of the errors that brings up reliable information. As a contribution, we obtain a final map that is more coherent with the reality of the ori...