Abstract
In recent years, the use of imaging sensors that produce a three-dimensional representation of the environment has become an efficient solution to increase the degree of perception of autonomous mobile robots. Accurate and dense 3D point clouds can be generated from traditional stereo systems and laser scanners or from the new generation of RGB-D cameras, representing a versatile, reliable and cost-effective solution that is rapidly gaining interest within the robotics community. For autonomous mobile robots, it is critical to assess the traversability of the surrounding environment, especially when driving across natural terrain. In this paper, a novel approach to detect traversable and non-traversable regions of the environment from a depth image is presented that could enhance mobility and safety through integration with localization, control and planning methods. The proposed algorithm is based on the analysis of the normal vector of a surface obtained through Principal Component Analysis and it leads to the definition of a novel, so defined, Unevenness Point Descriptor. Experimental results, obtained with vehicles operating in indoor and outdoor environments, are presented to validate this approach.
Highlights
Future autonomous mobile robots will take part in our daily lives and they will interact with much of our environment
In order to demonstrate the effectiveness of the UPDbased approach for terrain roughness evaluation, we first apply the system to simulated data
The Unevenness Point Descriptor (UPD) is experimentally validated in real experiments performed in both indoor and outdoor scenarios
Summary
Future autonomous mobile robots will take part in our daily lives and they will interact with much of our environment. Nadicvo.lraoIb. oGti.asnynsto.c, c2a0r1o3a,nVdoLl.u1ig0i,S2p8e4d:i2ca0t1o3: 1 Unevenness Point Descriptor for Terrain Analysis in Mobile Robot Applications view, describing a specific characteristic of an object in order to give a perceptive meaning is not easy. This issue is called “semantic perception” [8]. Non-traversable obstacles or highly irregular terrain can be detected by an autonomous vehicle along its path toward a target both in indoor and outdoor scenarios Research in this field generally refers to two indices: the roughness and the inclination index [9], [10].
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have