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Impact of Vehicle Dynamics Modelling on Feature Based SLAM for Autonomous Racing.
KTH, School of Industrial Engineering and Management (ITM).
KTH, School of Industrial Engineering and Management (ITM).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Fordonsmodelleringens påverkan på SLAM för autonom racing. (Swedish)
Abstract [en]

In autonomous racing there is a need to accurately localize the vehicle while simultaneously creating a map of the track. This information can be delivered to planning and control layers in order to achieve fully autonomous racing.

The kinematic model is a commonly used motion model in feature-based SLAM. However, it is a poor representation of the vehicle when considering high lateral accelerations since the model is only based on trigonometric relationships. This Master’s Thesis investigates the consequence of using the kinematic model when undertaking demanding maneuvers; and if by switching to a dynamic model, which takes the tire forces into account, can improve the localization performance.

An EKF-SLAM algorithm comprising the kinematic and dynamic model was implemented on a development platform. The pose estimation accuracy was compared using either model when subject to typical maneuvers in racing-scenarios.

The results showed that the pose estimation accuracy was in general similar when using either of the vehicle models. When exposed to large slip angles, the implications of switching from a kinematic model to a dynamic model resulted in a significantly better pose estimation accuracy when driving in an unknown environment. However, switching to a dynamic model had little effect when driving in a known environment.

The implications of the study suggest that, during the first lap of a racing track, the kinematic model should be switched to a dynamic model when subject to high lateral accelerations. For the consecutive laps, the choice of vehicle model has less impact.

Keywords: SLAM, EKF-SLAM, Localization, Estimation, Vehicle Dynamics,

Kinematic Model, Dynamic Model, Autonomous Racing

Abstract [sv]

I autonom racing är det viktigt att kunna lokalisera fordonet med hög noggrannhet samtidigt som en karta över banan skapas. Den här informationen kan vidare bli hanterad av planerings- och reglersystem för att uppfylla autonom racing fullt ut. Den kinematiska modellen är en vanligt förekommande rörelsemodell i SLAM. Den är däremot en bristande representation av fordonet vid höga laterala accelerationer eftersom modellen enbart är baserad på trigonometriska samband. Det här masterarbetet undersöker den kinematiska modellens påverkan vid olika manövrar och huruvida den dynamiska modellen, som modellerar däckkrafterna, kan förbättra prestandan.

En EKF-SLAM algorithm innehållande den kinematiska- och dynamiska modellen implementerades på en utvecklingsplattform. Estimeringsnoggrannheten av positionen och orienteringen jämfördes vid typiska manövrar för racingscenarier. Resultatet visade att estimeringsnoggrannheten av positionen och orienteringen var generellt sett lika vid användandet av antingen den kinematiska eller den dynamiska modellen. Implikationerna av att byta från den kinematiska modellen till den dynamiska modellen vid höga glidvinklar, resulterade i en signifikant bättre estimeringsnoggrannhet av positionen och orienteringen vid körning i en okänd miljö.

Emellertid så var effekterna av att byta till en dynamisk modell insignifikanta vid körning i en känd miljö. Implikationerna av denna studie föreslår att under det första varvet av racingbanan byta från den kinematiska modellen till den dynamiska vid höga laterala accelerationer. Under kommande varv har valet av fordonsmodell mindre effekt.

Nyckelord: SLAM, EKF-SLAM, lokalisering, estimering, fordonsmodellering, kinematisk modell, dynamisk modell, autonom racing.

Place, publisher, year, edition, pages
2019. , p. 80
Series
TRITA-ITM-EX ; 2019:133
Keywords [en]
SLAM, EKF-SLAM, Localization, Estimation, Vehicle Dynamics, Kinematic Model, Dynamic Model, Autonomous Racing.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-262646OAI: oai:DiVA.org:kth-262646DiVA, id: diva2:1361530
External cooperation
ÅF AB
Supervisors
Examiners
Available from: 2019-10-17 Created: 2019-10-16 Last updated: 2019-10-17Bibliographically approved

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