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Autonomous Robots

, Volume 41, Issue 4, pp 795–810 | Cite as

An incremental nonparametric Bayesian clustering-based traversable region detection method

  • Honggu Lee
  • Kiho KwakEmail author
  • Sungho JoEmail author
Article

Abstract

Navigation capability in complex and unknown outdoor environments is one of the major requirements for an autonomous vehicle and a robot that perform tasks such as a military mission or planetary exploration. Robust traversability estimation in unknown environments would allow the vehicle or the robot to devise control and planning strategies to maximize their effectiveness. In this study, we present a self-supervised on-line learning architecture to estimate the traversability in complex and unknown outdoor environments. The proposed approach builds a model by clustering appearance data using the newly proposed incremental nonparametric Bayesian clustering algorithm. The clusters are then classified as being either traversable or non-traversable. Because our approach effectively groups unknown regions with similar properties, while the vehicle is in motion without human intervention, the vehicle can be deployed to new environments by automatically adapting to changing environmental conditions. We demonstrate the performance of the proposed clustering algorithm through intensive experiments using synthetic and real data and evaluate the viability of the traversability estimation using real data sets collected in outdoor environment.

Keywords

Autonomous driving Incremental clustering Self-supervised learning Dirichlet process mixture model Traversability prediction 

Notes

Acknowledgments

This research was supported by the MOTIE (The Ministry of Trade, Industry and Energy), Korea, under the Technology Innovation Program supervised by KEIT (Korea Evaluation Institute of Industrial Technology),10045252, Development of robot task intelligence technology.

Supplementary material

Supplementary material 1 (mp4 19016 KB)

10514_2016_9588_MOESM2_ESM.mp4 (21.8 mb)
Supplementary material 2 (mp4 22310 KB)

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of ComputingKorea Advanced Institute of Science and Technology (KAIST)DaejeonSouth Korea
  2. 2.Agency for Defense DevelopmentDaejeonSouth Korea

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