An Efficient and Continuous Representation for Occupancy Mapping with Random Mapping

Generating meaningful spatial models of physical environments is a crucial ability for autonomous navigation of mobile robots. This paper considers the problem of building continuous occupancy maps from sparse and noisy sensor data. To this end, we propose a new method named random mapping maps that advances the popular methods in two aspects. Firstly, it can represent environment models in a memory-saving and time-saving manner by randomly mapping a low-dimensional feature space to a high-dimensional one where a linear model is learnt. Secondly, it can rapidly obtain accurate inferences of the occupancy states of the spatial locations. This technique is based on the random mapping that projects the measurement data into a random feature space in which a discriminative model is learnt by the available data. It can asymptotically represent the complexity of the real world as the mapping dimension increases. Evaluations of the proposed method were conducted on various environments to verify its availability to environment modeling. Its performances in terms of time and memory consumptions were evaluated quantitatively. Finally, as a practical application, experiments about path planning were conducted based on the gradients of the proposed representation of environment model.

Authors

• Xu Liu 0026
• Decai Li
• Yuqing He

Keywords

• Buildings
• Robot sensing systems
• Extraterrestrial measurements
• Path planning
• Data models
• Complexity theory
• Noise measurement
• Random Function
• Occupancy Map
• Feature Space
• Noisy Data
• Model Discrimination
• Low-dimensional Space
• Memory Consumption
• Random Feature
• Robot Navigation
• Occupancy State
• Dimensional Map
• Low-dimensional Feature Space
• Quantitative Results
• Nonlinear Function
• Random Method
• Grid Cells
• 3D Space
• Stochastic Gradient Descent
• Weight Vector
• Unstructured Environments
• Random Matrix
• Linear Classifier
• Random Vector
• Unmanned Ground Vehicles
• Unmanned Aerial Vehicles
• Gradient Descent Method
• Rapidly-exploring Random Tree
• Linearly Separable
• Large-scale Environments