Terrain Mapping and Classification in Outdoor Environments Using Neural Networks
نویسندگان
چکیده
This paper describes a three-dimensional terrain mapping and classification technique to allow the operation of mobile robots in outdoor environments using laser range finders. We propose the use of a multi-layer perceptron neural network to classify the terrain into navigable, partially navigable, and non-navigable. The maps generated by our approach can be used for path planning, navigation, and local obstacle avoidance. Experimental tests using an outdoor robot and a laser sensor demonstrate the accuracy of the presented methods.
منابع مشابه
Domain Adaptation For Mobile Robot Navigation
An important challenge in outdoor mobile robotic perception is maintaining terrain classification performance throughout the extremely variable conditions that we may wish a robot to operate under. Outdoor robots operate in a series of “environments” that consist of diverse terrain, vegetation, weather, and lighting conditions. A physical robot does not randomly jump between environments; typic...
متن کاملDeep Feature Learning for Acoustics-Based Terrain Classification
In order for robots to efficiently navigate in real-world environments, they need to be able to classify and characterize terrain for safe navigation. The majority of techniques for terrain classification is predominantly based on using visual features. However, as vision-based approaches are severely affected by appearance variations and occlusions, relying solely on them incapacitates the abi...
متن کاملMobile Robot Localisation and Mapping in Extensive Outdoor Environments
Tim Bailey Doctor of Philosophy The University of Sydney August 2002 Mobile Robot Localisation and Mapping in Extensive Outdoor Environments This thesis addresses the issues of scale for practical implementations of simultaneous localisation and mapping (SLAM) in extensive outdoor environments. Building an incremental map while also using it for localisation is of prime importance for mobile ro...
متن کاملMulti-View Face Detection in Open Environments using Gabor Features and Neural Networks
Multi-view face detection in open environments is a challenging task, due to the wide variations in illumination, face appearances and occlusion. In this paper, a robust method for multi-view face detection in open environments, using a combination of Gabor features and neural networks, is presented. Firstly, the effect of changing the Gabor filter parameters (orientation, frequency, standard d...
متن کاملEffect of sound classification by neural networks in the recognition of human hearing
In this paper, we focus on two basic issues: (a) the classification of sound by neural networks based on frequency and sound intensity parameters (b) evaluating the health of different human ears as compared to of those a healthy person. Sound classification by a specific feed forward neural network with two inputs as frequency and sound intensity and two hidden layers is proposed. This process...
متن کامل