Extracting semantic indoor maps from occupancy grids

2D Semantic Mapping on Occupancy Grids

In recent years, techniques for building metric maps of indoor environments have been intensely studied, and they perform very well in noumerous applications. Simultaneous Localization and Mapping (SLAM) methods produce globally consistent, metric maps of the explored environment. Although such maps describe how the environment looks like and can be used for navigation, there exist no abstracte...

Free Space Computation from Stochastic Occupancy Grids based on Iconic Kalman Filtered Disparity Maps

This paper presents a method for determining the free space in a scene as viewed by a vehicle-mounted camera. Using disparity maps from a stereo camera and known camera motion, the disparity maps are first filtered by an iconic Kalman filter, operating on each pixel individually, thereby reducing variance and increasing the density of the filtered disparity map. Then, a stochastic occupancy gri...

Indoor occupancy estimation from carbon dioxide concentration

This paper presents an indoor occupancy estimator with which we can estimate the number of real-time indoor occupants based on the carbon dioxide (CO2) measurement. The estimator is actually a dynamic model of the occupancy level. To identify the dynamic model, we propose the Feature Scaled Extreme Learning Machine (FS-ELM) algorithm, which is a variation of the standard Extreme Learning Machin...

Confidence-aware Occupancy Grids

Extracting Skeletons from Distance Maps

A skeleton is a useful shape descriptor that encodes both boundary and region information for a given object. A widely used approach for skeletonization is to use a distance transform. Although many skeletonization algorithms have been developed, most methods involve the computation of high order derivatives and the evaluation of complex expressions. In this paper, we propose an algorithm that ...