In this project is presented an approach to laser-based people tracking using a multi-hypothesis tracker that detects and tracks legs separately with Kalman filters, constant velocity motion models, a multi-hypothesis data association strategy, beside an extension of this multi-hypotesis tracker adding data fusion from different detectors. For the multi-hypothesis tracker people are defined as high-level tracks consisting of two legs that are found with little model knowledge, and for fusion people are defined by laser detections, vision detections and others. We extend the data association so that it explicitly handles track confirmation, deletions and fusion of information from different sensors in addition to detections. Additionally, we adapt the corresponding probabilities in a situation-dependent fashion so as to reflect the fact that in those systems appears many false alarms that are bad for the good association of real persons, and furthermore adapt the probabilities to taking in account that our laser detection system has not always have people detections. Also adapt the probabilities to add fusion of data from different detectors to better differentiate a specific person that would be the person that interact with the robot. In this project also presents an obstacle detector using a vertical laser for filtering laser detections of the floor obtained by a horizontal laser detector in punctual moments of robot inclinations due to the Segway that the robots used. These detections of the floor is suitable filter them because they can introduce false detections of people who can introduce errors in the detection of the real people who follow the tracker. The experimental results in matlab demonstrated how the obstacle detector performs a good detection of floor, walls and ramps. And the experimental results in matlab for the tracker demonstrated how the tracker implemented performs a more accurate tracking behavior in terms of people association errors in crosses between persons and number of track losses in similar situations. Furthermore, this experimental results in matlab shows that is possible incorporate the fusion of detections form different people detectors in the tracking algorithms. Finally, the results carried out in front of different obstacles using a mobile robot Dabo showed how the obstacle detector detect the floor and walls and perform a filter of the horizontal laser eliminating bad detections of the floor by this laser, because this detections are bad for the punctual appearance of false alarms in this situations. Furthermore, the experimental results carried out in different indoor and outdoor environments using a mobile robot Dabo illustrate that our tracking approach can robustly and efficiently track multiple people even in situations of high levels of false alarm detection and high levels of losses of detections by our laser detector system.