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Predictive Driving in an Unstructured Scenario Using the Bundle Adjustment Algorithm
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DOI：10.1109/TCST.2020.2977306.png)
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摘要
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In this article, the autonomous driving problem in an unstructured scene is addressed using a model predictive control (MPC) scheme. The lack of scene structure makes the sensing problem challenging, in particular when considered within a control loop. To circumvent this difficulty, the bundle adjustment (BA) algorithm from computer vision is used to detect obstacles and compute a sparse representation of the environment. In one of the main results of this article, it is shown how this sparse representation can be cast as additional safety constraints to the MPC optimization. The MPC/BA combination is intuitively appealing since they both solve quadratic problems and also because the BA estimations trend to be more accurate when the resulting constraints become active in the MPC solver. This article contains a theoretical presentation of the control scheme and discusses implementation details. An example of the overall approach at work can be seen in https://youtu.be/aU46vpzDHso.
Keyword:
Sensors
Autonomous vehicles
Cameras
Planning
Safety
Bundle adjustment
Navigation
Autonomous driving
bundle adjustment (BA)
model predictive control (MPC)
obstacle avoidance
structure from motion
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