An enhanced occupancy map for exploration via pose separation

We develop a new occupancy map that respects the role of the sensor measurement bearing and how it relates to the resolution of the existing occupancy map. We borrow an idea from Konolige for recording and tracking, in an occupancy-like map, the bearing at which sensor readings originate with respect to a given cell. Our specific contribution is in the way we process the sensor pose information, which is the bearing of the sensor readings when it indicates the presence of an obstacle in a particular cell. For each cell in the occupancy map, we calculate the greatest separation of incident poses, and then store that information in a new two-dimensional array called a pose map. A cell in the pose map measures the quality of information contained in the corresponding cell of the occupancy map. We merge the new pose map with the existing map to generate an enhanced occupancy map. Exploration plans derived from the enhanced occupancy map are more efficient and complete in that they do not guide the robot around phantom obstacles nor incorrectly classify narrow openings as closed commonly found in conventional occupancy maps.

Authors

• Robert Grabowski
• Pradeep K. Khosla
• Howie Choset

Keywords

• Robot sensing systems
• Robot kinematics
• Humans
• Fuses
• Surveillance
• Reconnaissance
• Sensor fusion
• Sonar measurements
• Navigation
• Occupancy Map
• Information Quality
• Sensor Readings
• Presence Of Obstacles
• Pose Information
• Frontier
• Points In Space
• Conditional Independence
• Assumption Of Independence
• Multiple Readings
• Degree Of Separation
• Sensor Model
• Conditional Dependence
• Occupancy Probability
• Polyline
• Occupancy Values
• Quality Point
• Separation Angle
• Small Robot