A mobile robot requires perception of its local environment for both sensor based locomotion and for position estimation. Occupancy grids, based on ultrasonic range data, provide a robust description of the local environment for locomotion. Unfortunately, current techniques for position estimation based on occupancy grids are both unreliable and computationally expensive. This paper reports on ...

This paper focuses on efficient occupancy grid building based on wavelet occupancy grids, a new sparse grid representation and on a new update algorithm for range sensors. The update algorithm takes advantage of the natural multiscale properties of the wavelet expansion to update only parts of the environement that are modified by the sensor measurements and at the proper scale. The sparse wave...

Two types of probabilistic maps are popular in the mobile robotics literature: occupancy grids and geometric maps. Occupancy grids have the advantages of simplicity and speed, but they represent only a restricted class of maps and they make incorrect independence assumptions. On the other hand, current geometric approaches, which characterize the environment by features such as line segments, c...

In this paper we present an approach to estimate Free Space from a Stereo image pair using stochastic occupancy grids. We do this in the domain of autonomous driving on the famous benchmark dataset KITTI. Later based on the generated occupancy grid we match 2 image sequences to compute the top view representation of the map. We do this to map the environment. We compute a transformation between...

Occupancy grid map is a popular tool for representing the surrounding environments of mobile robots/intelligent vehicles. Its applications can be dated back to the 1980s, when researchers utilized sonar or LiDAR to illustrate environments by occupancy grids. However, in the literature, research on vision-based occupancy grid mapping is scant. Furthermore, when moving in a real dynamic world, tr...

Occupancy grids are common frameworks for creating a map of the environment using a robot. Traditional occupancy grid mapping algorithms assume that map voxels are independent of each other. In addition, they utilize a map representation, wherein each voxel stores a single number representing the occupancy probability. This leads to conflicts in the map, and more importantly, inconsistency betw...