Algorithms and a framework for indoor robot mapping in a noisy environment using clustering in spatial and hough domains

Map generation by a robot in a cluttered and noisy environment is an important problem in autonomous robot navigation. This paper presents algorithms and a framework to generate 2D line maps from laser range sensor data using clustering in spatial (Euclidean) and Hough domains in noisy environments. The contributions of the paper are: (1) it shows the applicability of density-based clustering methods and mathematical morphological techniques generally used in image processing for noise removal from laser range sensor data; (2) it presents a new algorithm to generate straight-line maps by applying clustering in the spatial domain; (3) it presents a new algorithm for robot mapping using clustering in a Hough domain; and (4) it presents a new framework to load, delete, install or update appropriate kernels in the robot remotely from the server. The framework provides a means to select the most appropriate kernel and fine-tune its parameters remotely from the server based on online feedback, which proves to be very efficient in dynamic environments with noisy conditions. The accuracy and performance of the techniques presented in this paper are discussed with conventional line segment-based EKF-SLAM and the results are compared.