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Communication Dans Un Congrès Année : 2015

Road Invariant Extended Kalman Filter for an Enhanced Estimation of GPS Errors using Lane Markings

Résumé

Satellite positioning is a key technology for autonomous navigation in outdoors environments. When using standalone computation with mono-frequency receivers, positioning errors are not in accordance with the required performance. Nevertheless, since errors are strongly time-correlated, a GPS fix is quite informative if a shaping model of the positioning errors is carefully handled and made possible by exteroceptive sensors. When driving in a road with a camera detecting lane markings, the lateral error is directly observable by using a lane marking map. It can be well modeled by mixing auto-regressive and random constant models. An algebraic observability study is conducted to prove that this modeling is completely observable in a road frame. A new road invariant Extended Kalman Filter (EKF) is then presented to conserve the observability of every component of the state vector for any road whatever its orientation. The filter manages road changes by using bijective transformations that are detailed. Real experimental results indicate that the performance of the estimation process is significantly improved compared to a classic EKF that is implemented in a fixed working frame with a less informative error modeling.
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Dates et versions

hal-01213673 , version 1 (08-10-2015)

Identifiants

  • HAL Id : hal-01213673 , version 1

Citer

Zui Tao, Philippe Bonnifait. Road Invariant Extended Kalman Filter for an Enhanced Estimation of GPS Errors using Lane Markings. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015), Sep 2015, Hamburg, Germany. pp.3119-3124. ⟨hal-01213673⟩
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