Map merging using hough peak matching

One of the major problems for multi-robot SLAM is that the robots only know their positions in their own local coordinate frames, so fusing map data can be challenging. In this research, the mapping process is extended to multiple robots with a novel occupancy grid map fusion algorithm. Map fusion is achieved by transforming individual maps into the Hough space where they are represented in an abstract form. Properties of the Hough transform are used to find the common regions in the maps, which are then used to calculate the unknown transformation between the maps. Results are shown from tests performed on benchmark data sets and real-world experiments with multiple robotic platforms.

Authors

• Sajad Saeedi 0001
• Liam Paull
• Michael Trentini
• Mae L. Seto
• Howard Li

Keywords

• Robot kinematics
• Simultaneous localization and mapping
• Merging
• Transforms
• Correlation
• Entropy
• Map Merging
• Real-world Experiments
• Coordinate Frame
• Hough Transform
• Multiple Robots
• Occupancy Grid
• Peak Value
• Random Walk
• Correlation Results
• Exhaustive Search
• Indoor Environments
• Peak Location
• Line Segment
• Image Space
• Set Of Cells
• Particle Filter
• Peak Point
• Image Entropy
• Individual Robots