Evaluating SLAM algorithms for Autonomous Helicopters
Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Navigation with unmanned aerial vehicles (UAVs) requires good knowledge of the current position and other states. A UAV navigation system often uses GPS and inertial sensors in a state estimation solution. If the GPS signal is lost or corrupted state estimation must still be possible and this is where simultaneous localization and mapping (SLAM) provides a solution. SLAM considers the problem of incrementally building a consistent map of a previously unknown environment and simultaneously localize itself within this map, thus a solution does not require position from the GPS receiver.
This thesis presents a visual feature based SLAM solution using a low resolution video camera, a low-cost inertial measurement unit (IMU) and a barometric pressure sensor. State estimation in made with a extended information filter (EIF) where sparseness in the information matrix is enforced with an approximation.
An implementation is evaluated on real flight data and compared to a EKF-SLAM solution. Results show that both solutions provide similar estimates but the EIF is over-confident. The sparse structure is exploited, possibly not fully, making the solution nearly linear in time and storage requirements are linear in the number of features which enables evaluation for a longer period of time.
Place, publisher, year, edition, pages
Institutionen för systemteknik , 2008. , 60 p.
SLAM, UAV, Information Matrix, Covariance Matrix, Information Filter, Kalman Filter, Estimation
IdentifiersURN: urn:nbn:se:liu:diva-12282ISRN: LiTH-ISY-EX--08/4137--SEOAI: oai:DiVA.org:liu-12282DiVA: diva2:18513
2008-06-11, Algoritmen, Hus B, Linköping, 13:15 (English)
Törnqvist, DavidMolander, Sören