Multi-scale space smoothing and segmentation of range data for robot navigation - Tang, Fan (2010)
One of the most essential problems for mobile robot navigation is to enable an autonomous robot to navigate in an unknown environment and to incrementally build a map of this environment while simultaneously using this map to compute its current location. This problem is usually referred to as Simultaneous Localization and Mapping (SLAM). The feature-based SLAM approach has become one of the most promising solutions due to the tractability of its map. However it requires a robust method to extract enough robot pose invariant detectable landmarks from the surrounding environment. This thesis focuses on feature extraction methods from range data based on several filtering algorithms. Firstly, the bilateral filtering algorithm based on Kalman filter is proposed to extract robot pose invariant features from range data. With the ideas of multi-scale filtering from image processing, an adaptive smoothing algorithm, with a model based mask, within a scale space framework is proposed for feature extraction from range data. This algorithm smoothes range data and segments it at the same time by translating a model based mask over the data. The weights of the smoothing mask are adaptively calculated according to the Mahalanobis distance between range data and model based predictions. The model based mask smoothing technique with adaptive weights is applied in multi-scale space. The convergence of the algorithm is also proved in terms of its compliance with the anisotropic diffusion concept from the vision literature. Thus, more robust and pose-invariant features can be extracted.