Occupancy grid mapping with ultrasonic range finder

#OCCUPANCY GRID MAPPING WITH ULTRASONIC RANGE FINDER UPDATE#

Finally we present a labeling strategy while a robot is exploring an unknown environment.

#OCCUPANCY GRID MAPPING WITH ULTRASONIC RANGE FINDER UPDATE#

Then the classification results are combined probabilistically to update the semantic grid map. We use a robot-mounted laser range finder (LRF) data to learn and classify an environment into various area types. The classification accuracy is further improved by outlier rejection and topological correction. SGM is similar to an Occupancy Grid (OG) map, however with high level information as environment type labels. SGM is similar to an Occupancy Grid (OG) map, however with high level information as environment type labels. In this paper, we propose a Semantic Grid Map (SGM) to represent an environment. High level abstractions, which are commonly used by humans, can be learnt by robots to effectively communicate with humans.

Human robot interaction has become an important area of research in the robotics community. Click here to see an animation demonstrating the approach.IROS 2010 Workshop on Semantic Mapping and Autonomous Knowledge Acquisition, 2010, pp. This labeling is done using a Conditional Random Field that takes both shape and appearance information into account. Localization was done by tracking the rotation of the wheel. Environmental mapping was done using an occupancy grid method based on the algorithm detailed in Probabilistic Robotics. The laser points are projected into the camera images and then labeled by the type of object they point at. The goal of the RPiMapper project was to build a mobile robotics platform for autonomous mapping of a static environment using multiple ultrasonic range sensors. In collaboration with the Australian Centre for Field Robotics, we use a car equipped with a laser range finder and a camera to detect objects in urban environments. In addition to place labeling, we develop techniques that enable robots to detect differet objects in indoor environments, see publications below. Note that the robot has not 'seen' this environment before. The labeling and connectivity structure is used to generate a mixed topological / metric representation, as shown in the right image. The center image shows the resulting rooms (green), hallways (pink), and doorways (blue). Every point on the Voronoi graph is labeled using a Conditional Random Field. The left image shows an occupancy map along with the Voronoi graph (skeleton of the freespace) exracted from the map. The parameters of the underlying statistical model are learned from previously explored environments. The sensor returns a range r, and the angle of the. Ultrasonic range nders use time of ight to measure distance. Our method, termed arc carving, can be used to produce. In this case there was an obstacle detected in map cell (0,2). model similar to that commonly utilized by occupancy grids is used. By divid-ing the world into a uniform grid of cells and solving the mapping problem locally at each cell, occupancy grid mapping provides a representation of the environment that is general, robust, and easy to manipulate 1. The red line indicates the path taken by the laser, and the red dot is based on the the reported range value. Occupancy grid mapping is one of the most common methods of LIDAR mapping currently in use. The following figure illustrates the initial occupancy grid as well as the first sensor reading. It then labels every point on the Voronoi graph of this map by taking local shape and connectivity information into account. Consider a robot mapping its environment using a laser range finder.

To label the places in an indoor environment, our robot first builds an occupancy grid map using a laser range finder (click here for info on the mapping step). Such representations contain far more useful information than traditional maps, and enable robots to interact with humans in a more natural way. The goal of this project is to generate maps that describe environments in terms of objects and places.

Occupancy grid mapping with ultrasonic range finder