Vision-only autonomous navigation using topometric maps

This paper presents a mapping and navigation system for a mobile robot, which uses vision as its sole sensor modality. The system enables the robot to navigate autonomously, plan paths and avoid obstacles using a vision based topometric map of its environment. The map consists of a globally-consistent pose-graph with a local 3D point cloud attached to each of its nodes. These point clouds are used for direction independent loop closure and to dynamically generate 2D metric maps for locally optimal path planning. Using this locally semi-continuous metric space, the robot performs shortest path planning instead of following the nodes of the graph — as is done with most other vision-only navigation approaches. The system exploits the local accuracy of visual odometry in creating local metric maps, and uses pose graph SLAM, visual appearance-based place recognition and point clouds registration to create the topometric map. The ability of the framework to sustain vision-only navigation is validated experimentally, and the system is provided as open-source software.

Authors

• Feras Dayoub
• Timothy Morris
• Ben Upcroft
• Peter Corke

Keywords

• Navigation
• Visualization
• Measurement
• Simultaneous localization and mapping
• Mobile robots
• Autonomous Navigation
• Open-source
• Shortest Path
• Point Cloud
• Nodes In The Graph
• Path Planning
• Local Map
• Navigation System
• Mobile Robot
• Mapping System
• 3D Point Cloud
• Nonexpansive Mapping
• Loop Closure
• Visual Odometry
• Point Cloud Registration
• Global Map
• Current Function
• Particle Filter
• Localizer
• Topological Map
• Occupancy Grid
• Robot Operating System
• Ray Casting
• Global Path
• Laser Ranging
• Iterative Closest Point
• Stereopsis
• Layered System
• Obstacle Avoidance