Obstacle Avoidance and Trajectory Planning for an Indoor Mobile Robot Using Stereo Vision and Delaunay Triangulation

  • Michel Buffa
  • Olivier D. Faugeras
  • Zhengyou Zhang
Part of the Springer Series in Perception Engineering book series (SSPERCEPTION)


This article describes the work at INRIA on obstacle avoidance and trajectory planning for a mobile robot using stereo vision. Our mobile robot is equipped with a trinocular vision system that is being put into hardware and will be capable of delivering 3-D maps of the environment at rates between 1 and 5 Hz. The 3-D maps contain line segments extracted from the images and reconstructed in three dimensions. They are used for a variety of tasks, including obstacle avoidance and trajectory planning.

For those two tasks, we project on the ground floor the 3-D line segments to obtain a two-dimensional map, simplify the map according to some simple geometric criteria, and use the remaining 2-D segments to construct a tessellation, more precisely a triangulation, of the ground floor. This tessellation has several advantages.

It is adapted to the structure of the environment since all stereo segments are edges of triangles in the tessellation.

It can be efficiently computed (the algorithm we use has a complexity o(n) if n is the number of segments used).

It is dynamic in the sense that segments can be efficiently added or subtracted from an existing triangulation.

We use this triangulation as a support for further processing. We first determine free space simply by marking those triangles that are empty, again a very simple process, and then use the graph formed by those triangles to generate collision-free trajectories. When new sensory data are acquired, the ground floor map is easily updated using the nice computational properties of the Delaunay triangulation, and the process is iterated.

We show an example in which our robot navigates freely in a real indoor environment using this system.


Line Segment Mobile Robot Voronoi Diagram Obstacle Avoidance Delaunay Triangulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag New York, Inc. 1992

Authors and Affiliations

  • Michel Buffa
  • Olivier D. Faugeras
  • Zhengyou Zhang

There are no affiliations available

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