R-SLAM: Resilient localization and mapping in challenging environments

Balcilar M., Yavuz S., Amasyalı M. F., Uslu E., Çakmak F.

ROBOTICS AND AUTONOMOUS SYSTEMS, vol.87, pp.66-80, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 87
  • Publication Date: 2017
  • Doi Number: 10.1016/j.robot.2016.09.013
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.66-80
  • Keywords: SLAM, Odometry error, Pose correction, Particle filter
  • Yıldız Technical University Affiliated: Yes


Accurate pose estimation plays an important role in solution of simultaneous localization and mapping (SLAM) problem, required for many robotic applications. This paper presents a new approach called R-SLAM, primarily to overcome systematic and non-systematic odometry errors which are generally caused by uneven floors, unexpected objects on the floor or wheel-slippage due to skidding or fast turns. The hybrid approach presented here combines the strengths of feature based and grid based methods to produce globally consistent high resolution maps within various types of environments.