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...

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...

In the paper, we consider an occupancy-based approach for range data fusion, as it is used in mobile robotics. We tackle the major problem of this approach, which is the redundancy of stored and processed data caused by using the grid representation of the occupancy function, by proposing a parametric piece-wise linear representation. When applied to vision-based world exploration, the new repr...

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...

The problem of where a mobile robot should go to efficiently build a map of its surroundings is frequently addressed using entropy reduction techniques. However, in exploration problems where the goal is to find an object or objects of interest, such techniques can be a useful heuristic but are optimising the wrong quantity. An example of such a problem is an autonomous underwater vehicle (AUV)...

We describe techniques for performing mobile robot localization using occupancy grids that allow subpixel localization and uncertainty estimation in the pixelized pose space. The techniques are based on a localization method where matching is performed between the visible landmarks at the current robot position and a previously generated map of the environment. A likelihood function over the sp...

The capacity to know about the environment is a major requirement for robots and automated systems. Real scenes are complex to perceive, dynamic and large. Therefore to perceive those scenes, robots have access to complementary sensors such as sonar/laser range-finders, cameras or bumpers, but all these are always noisy. In automated navigation, the internal representation of the environment is...

In this paper, we outline the strategy used for obstacle avoidance on our small, non-holonomic test vehicle, the Autonomous Tractor. This strategy relies on the fusion of data from a stereo camera, a scanning laser range-finder and the vehicle odometry to create occupancy grids which describe the traversability of the terrain in the current vicinity of the vehicle. Knowledge of the vehicle’s ki...

Urbanization is a primary driver of landscape conversion, with far-reaching effects on landscape pattern and process, particularly related to the population characteristics of animals. Urbanization can alter animal movement and habitat quality, both of which can influence population abundance and persistence. We evaluated three important population characteristics (population density, site occu...

We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving. Our is spatio-temporal grid with each cell containing both the probability being occupied by any agent, and two-dimensional flow vector representing direction magnitude that cell. method successfully mitigates shortcomings two most commonly-used representati...