SLAMBench 3.0: Systematic Automated Reproducible Evaluation of SLAM Systems for Robot Vision Challenges and Scene Understanding

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • External authors:
  • Mihai Bujanca
  • Paul Gafton
  • Sajad Saeedi
  • Andy Nisbet
  • Bruno Bodin
  • Michael F O'Boyle
  • Andrew J Davison
  • Paul H Kelly
  • Mikel Luján

Abstract

As the SLAM research area matures and the number of SLAM systems available increases, the need for frameworks that can objectively evaluate them against prior work grows. This new version of SLAMBench moves beyond traditional visual SLAM, and provides new support for scene understanding and non-rigid environments (dynamic SLAM). More concretely for dynamic SLAM, SLAMBench 3.0 includes the first publicly available implementation of DynamicFusion, along with an evaluation infrastructure. In addition, we include two SLAM systems (one dense, one sparse) augmented with convolutional neural networks for scene understanding, together with datasets and appropriate metrics. Through a series of use-cases, we demonstrate the newly incorporated algorithms, visulation aids and metrics (6 new metrics, 4 new datasets and 5 new algorithms).

Bibliographical metadata

Original languageEnglish
Title of host publication2019 International Conference on Robotics and Automation (ICRA)
PublisherIEEE
DOIs
Publication statusPublished - 12 Aug 2019