In this paper, we describe and compare three different uncertainty calculi techniques to build occupancy grids of an unknown environment using sensory information provided by a ring of ultrasonic range-finders. These techniques are based on Bayesian theory, Dempster–Shafer theory of evidence, and fuzzy set theory. Several sensor models are presented to handle uncertainty according to the select...

Occupancy grids are the most common framework when it comes to creating a map of the environment using a robot. This paper studies occupancy grids from the motion planning perspective and proposes a mapping method that provides richer data (map) for the purpose of planning and collision avoidance. Typically, in occupancy grid mapping, each cell contains a single number representing the probabil...

Determining the categories of different parts of a scene and generating a continuous traversable region map in the physical coordinate system are crucial for autonomous vehicle navigation. This paper presents our efforts in these two aspects for an autonomous vehicle operating in open terrain environment. Driven by the ideas that have been proposed in our Cognitive Architecture, we have designe...

This paper proposes the underwater terrain map building method based on the occupancy grids by using 3D point cloud from the polar sonar sensor system. Because it is expensive to use current 3D underwater sonar sensors, the low-cost 3D sensor systems are needed for the generalized purposes. So the polar-styled sonar sensor system was designed and implemented. It was constructed on the rotating ...

We have previously developed the Cognitive Robotic System, or CRS, to use the Soar cognitive architecture for the control of unmanned vehicles. The system was able to demonstrate the applicability of Soar for unmanned vehicles by controlling a ground vehicle to navigate to a target location using GPS in the presence of obstacles, including a cul-de-sac. However, only fairly basic information ab...

Computing Occupancy grids with LiDAR data, is a popular strategy for environment representation. In the last two decades, several authors have proposed different methods to render sensed information into grids, seeking obtain computational efficiency or accurate modeling. However, no comparison regarding their performance under object detection in autonomous driving applications has been found ...

The majority of existing approaches to mobile robot mapping assume that the world is static, an assumption which does not hold in most practical application domains. In this paper we present a probabilistic grid-based approach for modeling dynamic environments representing both, the occupancy and the dynamics of the corresponding area. We describe the environment as a spatial grid and use a hid...

Evidential grids have recently shown interesting properties for mobile object perception. Evidential grids are a generalisation of Bayesian occupancy grids using Dempster– Shafer theory. In particular, these grids can handle efficiently partial information. The novelty of this article is to propose a perception scheme enhanced by geo-referenced maps used as an additional source of information, ...

In apreviouspaper, probabilistic occupancy modeling has been successfully extended to 3-D environments by means of a closed-form approximation of the probability distribution. In this paper, the closed-form approximation is revisited in order to provide more reliable and meaningful models. A merging strategy of local probabilistic occupancy grids originating from each sensor viewpoint is introd...

Occupancy grids are common tools used in robotics to represent the robot environment, and that may be plan trajectories, select additional measurements acquire, etc. However, deriving information about those occupancy from sensor often induce a lot of uncertainty, especially for grid elements correspond occluded or far away area robot. This means quite uncertain imprecise at some places, while ...