Laser based obstacle avoidance strategy for autonomous robot navigation using DBSCAN for versatile distance

Abstract

Towards fully autonomous navigation, guidance plays an important task for successful autonomous navigation. In this paper, the authors propose an obstacle avoidance strategy based on distance clustering analysis for safe autonomous robot navigation. Autonomous navigation systems must be able to recognize objects in order to perform a collision free motion in both unknown indoor/outdoor environments. Firstly, it was proposed to detect objects using the Density-based spatial clustering of applications with noise (DBSCAN) method through a dynamic density-reachable implementation. Secondly, in order to determine an optimal path for collision avoidance a distance clustering analysis was implemented. Subsequently, a set of possible waypoints were extracted in order to estimate the best path candidate. Preliminary results were gathered and tested on a group of consecutive frames. These specific methods of measurement were chosen to prove their effectiveness.