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On-board recursive state estimation for dead-reckoning in an autonomous truck
KTH, School of Electrical Engineering (EES).
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Most of fully-autonomous vehicles are equipped with GPS devices in order to keep track of their exact location while driving towards any target destination. However, it is widely known that GPS systems are likely to fail under certain conditions, e.g., in long tunnels or during very bad weather conditions. In this master thesis work we present an Extended Kalman filter (EKF) framework for dead-reckoning in au-tonomous trucks equipped with a CPU, a gyroscope and four simulated sensors: a GPS, a magnetometer and two rotary encoders for velocity. The EKF will fuse the sensors measurements with a prediction that uses the kinematic model of a non-holonomic vehicle. In order to improve the estimation of the yaw angular position when a GPS outage is reported a new calibration method based on the rotation matrix is applied. This method is proven to e˙ectively decrease the error while driving in GPS denied environments.The tests are performed in a real-time embedded system, NI myRIO, that runs on-board of a 1:14 scaled Scania truck. The performance results confirm the correctness of our framework under short-term GPS outages, during many driving loops. Additionally, during long-term outages the estimation works pretty well for one loop and it has a good performance for multiple loops due to the unavoidable sensor drifting.

Abstract [sv]

De flesta av fullt autonoma fordon är utrustade med GPS-enheter för att hålla reda på sin exakta position när man kör mot ett mål destina-tion. Det är dock allmänt känt att GPS-system kommer sannolikt att misslyckas på vissa villkor, till exempel, under långa tunnlar eller under mycket svåra väderförhållanden. I detta examensarbete presenteras ett Extended Kalman filter (EKF) ramverk för dead-reckoning i autono-ma lastbilar utrustade med ett gyroskop och fyra simulerade sensorer: en GPS, en magnetometer och två roterande pulsgivare för hastighet. EKF:n kommer säkring sensorerna mätningar med en förutsägelse som använder den kinematiska modellen av ett icke-holonomiskt fordon. För att förbättra uppskattningen av gir vinkelposition när en GPS-avbrott rapporteras testade vi en ny kalibreringsmetod baserad på rotationsma-trisen. Denna metod har visat sig att e˙ektivt minska fel vid körning i GPS förnekade miljöer.Testerna utförs i en realtid inbyggda system, NI myRIO, som går ombord på en 1:14 skalade Scanialastbil. Prestanda resultat bekräf-tar riktigheten av våra ramverk under kortfristiga GPS avbrott, under många driv loopar. Dessutom, vid långtidsavbrottuppskattningen fun-gerar ganska bra för en slinga och har en bra prestanda för flera slingor på grund av den oundvikliga sensorn drifting.

Place, publisher, year, edition, pages
2014. , 61 p.
Series
EES Examensarbete / Master Thesis, TRITA-XR-EE-RT 2014:012
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-188907OAI: oai:DiVA.org:kth-188907DiVA: diva2:940728
Examiners
Available from: 2016-06-21 Created: 2016-06-21 Last updated: 2016-06-21Bibliographically approved

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