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Location utility-based map reduction

T. Steiner,Guoquan Huang,J. Leonard

Published 2015 in IEEE International Conference on Robotics and Automation

ABSTRACT

Maps used for navigation often include a database of location descriptions for place recognition (loop closing), which permits bounded-error performance. A standard pose-graph SLAM system adds a new entry for every new pose into the location database, which grows linearly and unbounded in time and thus becomes unsustainable. To address this issue, in this paper we propose a new map-reduction approach that pre-constructs a fixed-size place-recognition database amenable to the limited storage and processing resources of the vehicle by exploiting the high-level structure of the environment as well as the vehicle motion. In particular, we introduce the concept of location utility - which encapsulates the visitation probability of a location and its spatial distribution relative to nearby locations in the database - as a measure of the value of potential loop-closure events to occur at that location. While finding the optimal reduced location database is NP-hard, we develop an efficient greedy algorithm to sort all the locations in a map based on their relative utility without access to sensor measurements or the vehicle trajectory. This enables pre-determination of a generic, limited-size place-recognition database containing the N best locations in the environment. To validate the proposed approach, we develop an open-source street-map simulator using real city-map data and show that an accurate map (pose-graph) can be attained even when using a place-recognition database with only 1% of the entries of the corresponding full database.

PUBLICATION RECORD

  • Publication year

    2015

  • Venue

    IEEE International Conference on Robotics and Automation

  • Publication date

    2015-05-26

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.1109/ICRA.2015.7139223
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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