Pose invariant, robust feature extraction from data with a modified scale space approach

Feature-based simultaneous localization and map building (SLAM) approaches require a robust method to extract position invariant landmarks from the surrounding environment. 2D laser range finders are currently one of the most common sensors used to obtain environmental information for mobile robot navigation due to their reliability, accuracy and low cost. However, the 2D laser scan data only give very limited information, making it difficult to extract meaningful features particularly in unstructured environments. The most important steps to extract features are segmentation and noise reduction. Scale space and adaptive smoothing are two common techniques within the vision community. They are used to remove high frequency noise and represent image data in multi-scale spaces. They allow for an easier segmentation of images and the extraction of features in the appropriate scale. In this paper, a modified adaptive smoothing algorithm is proposed and applied to laser range data within a modified scale space framework. This algorithm smoothes range data and segments it at the same time by translating a line model mask over the range data. Lines can be extracted from the segments by using a standard fitting algorithm.