Kratos: Princeton University's entry in the 2008 intelligent ground vehicle competition

Christopher A. Baldassano, Gordon H. Franken, Jonathan R. Mayer, Andrew M. Saxe, Derrick D. Yu

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations

Abstract

In this paper we present Kratos, an autonomous ground robot capable of static obstacle field navigation and lane following. A sole color stereo camera provides all environmental data. We detect obstacles by generating a 3D point cloud and then searching for nearby points of differing heights, and represent the results as a cost map of the environment. For lane detection we merge the output of a custom set of filters and iterate the RANSAC algorithm to fit parabolas to lane markings. Kratos' state estimation is built on a square root central difference Kalman filter, incorporating input from wheel odometry, a digital compass, and a GPS receiver. A 2D A* search plans the straightest optimal path between Kratos' position and a target waypoint, taking vehicle geometry into account. A novel C++ wrapper for Carnegie Mellon's IPC framework provides flexible communication between all services. Testing showed that obstacle detection and path planning were highly effective at generating safe paths through complicated obstacle fields, but that Kratos tended to brush obstacles due to the proportional law control algorithm cutting turns. In addition, the lane detection algorithm made significant errors when only a short stretch of a lane line was visible or when lighting conditions changed. Kratos ultimately earned first place in the Design category of the Intelligent Ground Vehicle Competition, and third place overall.

Original languageEnglish (US)
Article number72520I
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume7252
DOIs
StatePublished - Apr 7 2009
EventIntelligent Robots and Computer Vision XXVI: Algorithms and Techniques - San Jose, CA, United States
Duration: Jan 19 2009Jan 20 2009

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Cost map
  • IGVC
  • IPC
  • Kalman Filter
  • Lane detection
  • Path planning
  • Robotics
  • Stereo vision

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