FastSLAM Using Compressed Occupancy Grids

Confidence-aware Occupancy Grids

Finding freedom: Freespace segmentation using occupancy grids from stereo disparities

This project presents an implementation of the freespace segmentation approach proposed in [1]. This segmentation approach uses an occupancy grid representation which only requires a stereo disparity image and associated camera model for freespace computation, making it more computationally efficient than some state-of-the-art approaches using CNNs. Different segmentation algorithms were evalua...

A Comparison of Position Estimation Techniques Using Occupancy Grids

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 ...

Free Space Computation Using Stochastic Occupancy Grids and Dynamic Programming

The computation of free space available in an environment is an essential task for many intelligent automotive and robotic applications. This paper proposes a new approach, which builds a stochastic occupancy grid to address the free space problem as a dynamic programming task. Stereo measurements are integrated over time reducing disparity uncertainty. These integrated measurements are entered...

Comparison of Position Estimation Techniques Using Occupancy Grids

This paper addresses the problem of perception and localisation for a mobile robot in an unknown environment. It describes a modelling of the environment based on two certainty grids: one local model and the other one a global model. Localisation of the robot is reduced to nding the best alignment of the local certainty grid onto the global certainty grid. Four localisation procedures to correc...