Linear Features Observation Model for Autonomous Vehicle Localization
Precise localization is a core ability of an autonomous vehicle. It is a prerequisite for motion planning and execution. The well-established localization approaches such as Kalman and particle filters require a probabilistic observation model allowing to compute a likelihood of measurement given a...
Saved in:
Main Authors | , , |
---|---|
Format | Journal Article |
Language | English |
Published |
28.02.2020
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Precise localization is a core ability of an autonomous vehicle. It is a
prerequisite for motion planning and execution. The well-established
localization approaches such as Kalman and particle filters require a
probabilistic observation model allowing to compute a likelihood of measurement
given a system state vector, usually vehicle pose, and a map. The higher
precision of the localization system may be achieved through the development of
a more sophisticated observation model considering various measurement error
sources. Meanwhile model needs to be simple to be computable in real-time. This
paper proposes an observation model for visually detected linear features.
Examples of such features include, but not limited to, road markings and road
boundaries. The proposed observation model depicts two core detection error
sources: shift error and angular error. It also considers the probability of
false-positive detection. The structure of the proposed model allows
precomputing and incorporating the measurement error directly into the map
represented by a multichannel digital image. Measurement error precomputation
and storing the map as an image speeds up observation likelihood computation
and in turn localization system. The experimental evaluation on real autonomous
vehicle demonstrates that the proposed model allows for precise and reliable
localization in a variety of scenarios. |
---|---|
DOI: | 10.48550/arxiv.2002.12731 |