@conference {2879, title = {Indoor Real-Time Localisation for Multiple Autonomous Vehicles Fusing Vision, Odometry and IMU Data}, booktitle = {International Workshop on Modelling and Simulation for Autonomous Systems MESAS 2016}, volume = {9991 of the book series Lecture Notes in Computer Science (LNCS)}, year = {2016}, pages = {288 - 297}, publisher = {Springer}, organization = {Springer}, address = {Rome, Italy, June 15-16, 2016}, abstract = {

Due to the increasing usage of service and industrial autonomous vehicles, a precise localisation is an essential component required in many applications, e.g. indoor robot navigation. In open outdoor environments, differential GPS systems can provide precise positioning information. However, there are many applications in which GPS cannot be used, such as indoor environments. In this work, we aim to increase robot autonomy providing a localisation system based on passive markers, that fuses three kinds of data through extended Kalman filters. With the use of low cost devices, the optical data are combined with other robots{\textquoteright} sensor signals, i.e. odometry and inertial measurement units (IMU) data, in order to obtain accurate localisation at higher tracking frequencies. The entire system has been developed fully integrated with the Robotic Operating System (ROS) and has been validated with real robots.

}, keywords = {Embedded Control, Robotics}, doi = { 10.1007/978-3-319-47605-6_24}, url = {http://link.springer.com/chapter/10.1007/978-3-319-47605-6_24}, author = {A. Faralli and N. Giovannini and S. Nardi and L. Pallottino} }