Navigating by touch: Haptic Monte Carlo localization via geometric sensing and terrain classification

Russell Buchanan*, Jakub Bednarek, Marco Camurri, Michał R. Nowicki, Krzysztof Walas, Maurice Fallon

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Legged robot navigation in extreme environments can hinder the use of cameras and lidar due to darkness, air obfuscation or sensor damage, whereas proprioceptive sensing will continue to work reliably. In this paper, we propose a purely proprioceptive localization algorithm which fuses information from both geometry and terrain type to localize a legged robot within a prior map. First, a terrain classifier computes the probability that a foot has stepped on a particular terrain class from sensed foot forces. Then, a Monte Carlo-based estimator fuses this terrain probability with the geometric information of the foot contact points. Results demonstrate this approach operating online and onboard an ANYmal B300 quadruped robot traversing several terrain courses with different geometries and terrain types over more than 1.2 km. The method keeps pose estimation error below 20 cm using a prior map with trained network and using sensing only from the feet, leg joints and IMU.

Original languageEnglish
Pages (from-to)843-857
Number of pages15
JournalAutonomous Robots
Volume45
Issue number6
DOIs
Publication statusPublished - 27 Aug 2021

Keywords / Materials (for Non-textual outputs)

  • Legged robots
  • Proprioceptive localization
  • Tactile sensing
  • Terrain classification

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