Real-time monocular visual odometry for on-road vehicles with 1-point RANSAC

  • Authors:

  • Davide Scaramuzza;Friedrich Fraundorfer;Roland Siegwart

  • Affiliations:

  • Autonomous Systems Lab, ETH Zurich;Computer Vision and Geometry Group, ETH Zurich;Autonomous Systems Lab, ETH Zurich

  • Venue:
  • ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
  • Year:
  • 2009

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Abstract

This paper presents a system capable of recovering the trajectory of a vehicle from the video input of a single camera at a very high frame-rate. The overall frame-rate is limited only by the feature extraction process, as the outlier removal and the motion estimation steps take less than 1 millisecond with a normal laptop computer. The algorithm relies on a novel way of removing the outliers of the feature matching process. We show that by exploiting the nonholonomic constraints of wheeled vehicles it is possible to use a restrictive motion model which allows us to parameterize the motion with only 1 feature correspondence. Using a single feature correspondence for motion estimation is the lowest model parameterization possible and results in the most efficient algorithms for removing outliers. Here we present two methods for outlier removal. One based on RANSAC and the other one based on histogram voting. We demonstrate the approach using an omnidirectional camera placed on a vehicle during a peak time tour in the city of Zurich. We show that the proposed algorithm is able to cope with the large amount of clutter of the city (other moving cars, buses, trams, pedestrians, sudden stops of the vehicle, etc.). Using the proposed approach, we cover one of the longest trajectories ever reported in real-time from a single omnidirectional camera and in cluttered urban scenes, up to 3 kilometers.