Inverse Perspective Mapping Roll Angle Estimation for Motorcycles

© 2018 IEEE. This paper presents an image-based approach to estimate the motorcycle roll angle. The algorithm estimates directly the absolute roll to the road plane by means of a basic monocular camera. This means that the estimated roll angle is not affected by the road bank which is often a proble...

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Bibliographic Details
Main Authors: Damon, Pierre-Marie (Author), Hadj-Abdelkader, Hicham (Author), Arioui, Hichem (Author), Youcef-Toumi, Kamal (Author)
Other Authors: MIT Materials Research Laboratory (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2021-12-21T20:46:42Z.
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Online Access:Get fulltext
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100 1 0 |a Damon, Pierre-Marie  |e author 
100 1 0 |a MIT Materials Research Laboratory  |e contributor 
700 1 0 |a Hadj-Abdelkader, Hicham  |e author 
700 1 0 |a Arioui, Hichem  |e author 
700 1 0 |a Youcef-Toumi, Kamal  |e author 
245 0 0 |a Inverse Perspective Mapping Roll Angle Estimation for Motorcycles 
260 |b Institute of Electrical and Electronics Engineers (IEEE),   |c 2021-12-21T20:46:42Z. 
856 |z Get fulltext  |u https://hdl.handle.net/1721.1/137988.2 
520 |a © 2018 IEEE. This paper presents an image-based approach to estimate the motorcycle roll angle. The algorithm estimates directly the absolute roll to the road plane by means of a basic monocular camera. This means that the estimated roll angle is not affected by the road bank which is often a problem for vehicle observation and control purposes. For each captured image, the algorithm uses a numeric roll loop based on some simple knowledge of the road geometry. For each iteration, a bird-eye-view of the road is generated with the inverse perspective mapping technique. Then, a road marker filter associated with the well-known clothoid model are used respectively to track the road separation lanes and approximate them with mathematical functions. Finally, the algorithm computes two distinct areas between the two-road separation lanes. Its performances are tested by means of the motorcycle simulator BikeSim. This approach is very promising since it does not require any vehicle or tire model and is free of restrictive assumptions on the dynamics. 
546 |a en 
655 7 |a Article 
773 |t 10.1109/icarcv.2018.8581182 
773 |t 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018