Relative localization using path odometry information

Nakju Lett Doh, Howie Choset, Wan Kyun Chung

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

All mobile bases suffer from localization errors. Previous approaches to accommodate for localization errors either use external sensors such as lasers or sonars, or use internal sensors like encoders. An encoder's information is integrated to derive the robot's position; this is called odometry. A combination of external and internal sensors will ultimately solve the localization error problem, but this paper focuses only on processing the odometry information. We solve the localization problem by forming a new odometry error model for the synchro-drive robot then use a novel procedure to accurately estimate the error parameters of the odometry error model. This new procedure drives the robot through a known path and then uses the shape of the resulting path to estimate the model parameters. Experimental results validate that the proposed method precisely estimates the error parameters and that the derived odometry error model of the synchro-drive robot is correct.

Original languageEnglish
Pages (from-to)143-154
Number of pages12
JournalAutonomous Robots
Volume21
Issue number2
DOIs
Publication statusPublished - 2006 Sep
Externally publishedYes

Keywords

  • Differential drive robot
  • Generalized voronoi graph
  • Mobile robot
  • Odometry calibration
  • Odometry error model
  • Relative localization
  • Synchro-drive robot

ASJC Scopus subject areas

  • Artificial Intelligence

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