Tartalo: the door knocker robot

Abstract

Bioinspired navigation strategies require landmark identification subsystems for goal identification. Door recognition is a key problem to be solved during mobile robot navigation. Doors give access to many locations that are defined as goals for the robot. This paper presents an approach to door identification by means of recognition of the door handle. Rather than using the lines defined by the door blades, the region of interest of an image is extracted by means of the Hough transform and afterwards SIFT keypoints are obtained and matched against a database in order to positively recognize the door. The extraction of the ROI highly reduces the computational load of the SIFT algorithm and increases the handle recognition performance. The approach is evaluated and tested on a real Peoplebot robot, using a behavior-based control architecture that combines the identification module with free-space balancing (for corridor following), door knocking and crossing behaviors.Index Terms-Scale invariant feature transform, behaviorbased robotics

Vision Based Reconstruction Multi-Clouds of Scale Invariant Feature TransformFeatures for Indoor Navigation

“…These features are extracted in many ways; the common ways are Harries and SIFT extraction of image features [1,8] . Many navigations used SIFT feature descriptors as in [9,20] other papers now proposed approaches for how to deal with the path planning in the graph for the robots, or games as in [4,13] search algorithms like A* and Dijkstra were considered for the path planning algorithm. The heuristic search like A* tries to find the cheapest path to the goal.…”