Clustering Based Line Detection in Noisy Datasets

Abstract

For autonomous navigation of a mobile robot in indoor environments, accurate mapping of indoor structures is a crucial requirement. When robots are operating in a noisy cluttered environments the noise generated from the external sensors affect extracting features from sensor data. In this work, we present a novel method of extracting line segments from LIDAR data in very noisy datasets to accomplish accurate mapping. As compared to traditional methods of extracting line features from sensor data, our method can generate very accurate line features even in a very cluttered areas. We present a novel hybrid algorithm that uses Hough transform and clustering techniques that is fast and efficient.