Landmark-based homing navigation using omnidirectional depth information

Changmin Lee, Seung Eun Yu, Dae Eun Kim

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


A number of landmark-based navigation algorithms have been studied using feature extraction over the visual information. In this paper, we apply the distance information of the surrounding environment in a landmark navigation model. We mount a depth sensor on a mobile robot, in order to obtain omnidirectional distance information. The surrounding environment is represented as a circular form of landmark vectors, which forms a snapshot. The depth snapshots at the current position and the target position are compared to determine the homing direction, inspired by the snapshot model. Here, we suggest a holistic view of panoramic depth information for homing navigation where each sample point is taken as a landmark. The results are shown in a vector map of homing vectors. The performance of the suggested method is evaluated based on the angular errors and the homing success rate. Omnidirectional depth information about the surrounding environment can be a promising source of landmark homing navigation. We demonstrate the results that a holistic approach with omnidirectional depth information shows effective homing navigation.

Original languageEnglish
Article number1928
JournalSensors (Switzerland)
Issue number8
Publication statusPublished - 2017 Aug 22

Bibliographical note

Funding Information:
Acknowledgments: This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Education, Science and Technology (MEST)) (No. 2014R1A2A1A11053839).

Publisher Copyright:
© 2017 by the authors. Licensee MDPI, Basel, Switzerland.

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Information Systems
  • Instrumentation
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering
  • Biochemistry


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