Rough Terrain Perception Through Geometric Entities for Robot Navigation
Roberto Valencia-Murillo, Nancy Arana-Daniel, Carlos López-Franco, Alma Y. Alanís
Available Online July 2013.
- https://doi.org/10.2991/cse.2013.69How to use a DOI?
- Autonomous Navigation; Mobile Robots; Conformal Geometric Algebra; Learning From Demonstration
- This paper presents the implementation of a non-linear geometric cost function to be used with a learning to search algorithm (LEARCH) to robot navigation in rough terrains. The non-linear function introduced is a neural network trained with geometric entities as inputs (points, lines, spheres, planes). These inputs were codified using the Conformal Geometric Algebra framework in order to describe the features of the rough environment where the robot is going to navigate. The geometric entities contain implicitly more information about rough terrain that simple features obtained with image edge-detectors, furthermore by using them as descriptors, the dimension of the feature space is greatly reduced with regard to the dimension of features obtained with sophisticated feature detectors as SIFT or SURF. The advantages of using geometric entities with LEARCH algorithm are shown in the experimental results section of this paper.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Roberto Valencia-Murillo AU - Nancy Arana-Daniel AU - Carlos López-Franco AU - Alma Y. Alanís PY - 2013/07 DA - 2013/07 TI - Rough Terrain Perception Through Geometric Entities for Robot Navigation BT - 2nd International Conference on Advances in Computer Science and Engineering (CSE 2013) PB - Atlantis Press SP - 309 EP - 314 SN - 1951-6851 UR - https://doi.org/10.2991/cse.2013.69 DO - https://doi.org/10.2991/cse.2013.69 ID - Valencia-Murillo2013/07 ER -