Efficient Multiresolution Scrolling Grid for Stereo Vision-based MAV Obstacle Avoidance

Fast, aerial navigation in cluttered environments requires a suitable map representation for path planning. In this paper, we propose the use of an efficient, structured multiresolution representation that expands the sensor range of dense local grids for memory-constrained platforms. While similar data structures have been proposed, we avoid processing redundant occupancy information and use the organization of the grid to improve efficiency. By layering 3D circular buffers that double in resolution at each level, obstacles near the robot are represented at finer resolutions while coarse spatial information is maintained at greater distances. We also introduce a novel method for efficiently calculating the Euclidean distance transform on the multiresolution grid by leveraging its structure. Lastly, we utilize our proposed framework to demonstrate improved stereo camera-based MAV obstacle avoidance with an optimization-based planner in simulation.

Authors

• Eric Dexheimer
• Joshua G. Mangelson
• Sebastian A. Scherer
• Michael Kaess

Keywords

• Three-dimensional displays
• Transforms
• Organizations
• Robot sensing systems
• Path planning
• Spatial resolution
• Collision avoidance
• Obstacle Avoidance
• Scrolling
• Micro Air Vehicles
• Finer Resolution
• Redundant Information
• Distance Map
• Map Representation
• Dense Grid
• Local Grid
• Occupational Information
• Smoothing
• Parental Cells
• Final Level
• Manhattan Distance
• Trajectory Optimization
• Parabola
• Uniform Grid
• Odometry
• Scanning Direction
• Stereo Camera
• Lower Envelope
• Occupancy Grid
• Ray Casting
• 1D Array
• Finest Level
• World Coordinate
• Fine Cell
• Grid Layer
• Qualitative Examples
• Additional Memory