Mircea Popa

Laser Based Localization Techniques for Indoor Mobile Robots

The localization problem in indoor environment based on LIDAR measurements is analyzed in this paper. Practical aspects of the localization are discussed including the implementations of the state estimation and registration algorithms. The localization framework developed is sufficient generic to be used in a variety of other autonomous vehicles. The results of the proposed navigation algorithms demonstrate a reliable and accurate position estimation for autonomous vehicles operating in a variety of environments.

Visual odometer system to build feature based maps for mobile robot navigation

This paper presents a visual odometer system for mobile robot position correction. The developed algorithm detects the same Speeded Up Robust Features (SURF) on the stereo pair images to obtain three dimensional point clouds at every robot location. The algorithm tracks the displacement of the identical features viewed from different positions to compute the robots positions. The displacements between the point clouds are computed with the use of the Iterative Closest Point (ICP) algorithm. The ICP is used also to register the landmarks in the feature based map of the entire environment. The results of an indoor office environment experiments are shown.

An effective method for people detection in grayscale image sequences

This paper presents a method for detecting people from images taken with a camera mounted on a robot. The purpose of the detection is avoiding people collision while robot is moving within an unknown environment. It combines two algorithms for this purpose. First, the appearance of people is learned using a set of Haar-like features and the Adaboost algorithm. This information is embedded by building a classifier to differentiate people appearances by other structures. When an image is analyzed for detecting people, regions which contain vertical structures are determined using image gradients. Those regions which have a specific aspect-ratio are selected and the classifier is applied on them. The classifier marks the regions which contain people-like structures. Because this method is desired to be integrated in an autonomous robot navigation system for a dynamic environment, particular attention is paid to increase the speed of the detection as much as possible.

Position Estimation Techniques for Mobile Robots

The robot localization problem represents a key aspect in making a robot really autonomous. The position of the robot has to be estimated accurately based on the information about the surrounding world obtained from the sensors.