Sensor Based Adaptive Metric-Topological Cell Decomposition Method for Semantic Annotation of Structured Environments
2014 (English)In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV), Piscataway, NJ: IEEE Press, 2014, 1771-1777 p.Conference paper (Refereed)
A fundamental ingredient for semantic labeling is a reliable method for determining and representing the relevant spatial features of an environment. We address this challenge for planar metric-topological maps based on occupancy grids. Our method detects arbitrary dominant orientations in the presence of significant clutter, fits corresponding line features with tunable resolution, and extracts topological information by polygonal cell decomposition. Real-world case studies taken from the target application domain (autonomous forklift trucks in warehouses) demonstrate the performance and robustness of our method, while results from a preliminary algorithm to extract corridors, and junctions, demonstrate its expressiveness. Contribution of this work starts with the formulation of metric-topological surveying of environment, and a generic n-direction planar representation accompanied with a general method for extracting it from occupancy map. The implementation also includes some semantic labels specific to warehouse like environments. © 2014 IEEE.
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2014. 1771-1777 p.
Signal Processing Robotics
IdentifiersURN: urn:nbn:se:hh:diva-26597DOI: 10.1109/ICARCV.2014.7064584ScopusID: 2-s2.0-84946687398ISBN: 978-147995199-4OAI: oai:DiVA.org:hh-26597DiVA: diva2:750189
13th International Conference on Control, Automation, Robotics and Vision, ICARCV 2014, Marina Bay Sands, Singapore, December 10-12, 2014
This work was supported by the Swedish Knowledge Foundation and industry partners Kollmorgen, Optronic, and Toyota Material Handling Europe. Article number: 70645842014-09-262014-09-262016-10-08Bibliographically approved