Authors:
Timo Korthals
;
Marvin Barther
;
Thomas Schöpping
;
Stefan Herbrechtsmeier
and
Ulrich Rückert
Affiliation:
Bielefeld University, Germany
Keyword(s):
Occupancy Grid Mapping, Inverse Sensor Model, Inverse Particle Filter, Uncertain Range Sensors.
Related
Ontology
Subjects/Areas/Topics:
Adaptive Signal Processing and Control
;
Informatics in Control, Automation and Robotics
;
Mobile Robots and Autonomous Systems
;
Perception and Awareness
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
A huge number of techniques for detecting and mapping obstacles based on LIDAR and SONAR exist, though
not taking approximative sensors with high levels of uncertainty into consideration. The proposed mapping
method in this article is undertaken by detecting surfaces and approximating objects by distance using sensors
with high localization ambiguity. Detection is based on an Inverse Particle Filter, which uses readings from
single or multiple sensors as well as a robot’s motion. This contribution describes the extension of the Sequential
Importance Resampling filter to detect objects based on an analytical sensor model and embedding into
Occupancy Grid Maps. The approach has been applied to the autonomous mini robot AMiRo in a distributed
way. There were promising results for its low-power, low-cost proximity sensors in various real life mapping
scenarios, which outperform the standard Inverse Sensor Model approach.