H-SLAM: Rao-Blackwellized Particle Filter SLAM Using Hilbert Maps
by
Guillem Vallicrosa *
and Pere Ridao
Guillem Vallicrosa * and Pere Ridao
Underwater Robotics Research Center (CIRS), Computer Vision and Robotics Institute (VICOROB), Universitat de Girona, 17004 Girona, Spain
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1386; https://doi.org/10.3390/s18051386
Received: 28 March 2018 / Revised: 24 April 2018 / Accepted: 28 April 2018 / Published: 1 May 2018
(This article belongs to the Special Issue Underwater Sensing, Communication, Networking and Systems)
Occupancy Grid maps provide a probabilistic representation of space which is important for a variety of robotic applications like path planning and autonomous manipulation. In this paper, a SLAM (Simultaneous Localization and Mapping) framework capable of obtaining this representation online is presented. The H-SLAM (Hilbert Maps SLAM) is based on Hilbert Map representation and uses a Particle Filter to represent the robot state. Hilbert Maps offer a continuous probabilistic representation with a small memory footprint. We present a series of experimental results carried both in simulation and with real AUVs (Autonomous Underwater Vehicles). These results demonstrate that our approach is able to represent the environment more consistently while capable of running online.
View Full-Text
Keywords:
AUV (Autonomous Underwater Vehicle); SLAM (Simultaneous Localization and Mapping); PF (Particle Filter); 2D
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Vallicrosa, G.; Ridao, P. H-SLAM: Rao-Blackwellized Particle Filter SLAM Using Hilbert Maps. Sensors 2018, 18, 1386.
Show more citation formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
