Fast Position Tracking of an Autonomous Vehicle in Cluttered and Dynamic Indoor Environments

An augmented state vector approach to GPS-based localization

“…A similar system, relying on an EKF estimator to correct the robot pose, has been implemented on board the ANSER rover. In particular, [11] describes in details how segment-like features are extracted from raw laser data and compared with the a-priori model: 1) line extraction produces a set of lines { j l }; 2) the Mahalanobis distance associated to each couple of line ( j l , i m ) is computed (where { i m } is a set of oriented segment lines that define the a-priori map); 3) for each j l , the line i m for which such distance is minimum is selected and fed to the EKF.…”

Robot-Assisted Surveillance in Large Environments

“…Conversely, for each line feature l j originating from laser data, the map line m i that best matches l j can be expressed using two parameters representing, respectively, the distance ρ from the robot and the angle α between the line itself and the robot's heading [11]:…”

“…The mobile robot is equipped with two laser rangefinders (see Figure 5 at the top left): the former -used for surveillance -is hidden within the chassis and it is located about 50 cm above the ground, whereas the latter -used for self-localizationis located on top of a pole about 2 m high. As soon as new laser measurements are available, (i) line extraction produces a number of lines {l j } using a common Split & Merge algorithm; (ii) the Mahalanobis distance associated with each tuple (l j , m i ) is computed; (iii) for each l j , the line m i for which such a distance is minimum is selected and fed to the EKF, which can then update the robot pose using a line measurement model and the actual measure l j [11].…”