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Co-design of architectures and algorithms for mobile robot localization and model-based detection of obstacles

Daniel Tortei 1
1 LAAS-RAP - Équipe Robotique, Action et Perception
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : An autonomous mobile platform is endowed with a navigational system which must contain multiple functional bricks: perception, localization, path planning and motion control. As soon as such a robot or vehicle moves in a crowded environment, it continously loops several tasks in real time: sending reference values to motors' actuators, calculating its position in respect to a known reference frame and detection of potential obstacles on its path. Thanks to semantic richness provided by images and to low cost of visual sensors, these tasks often exploit visual cues. Other embedded systems running on these mobile platforms thus demand for an additional integration of high-speed embeddable processing systems capable of treating abundant visual sensorial input in real-time. Moreover, constraints influencing the autonomy of the mobile platform impose low power consumption. This thesis proposes SOPC (System on a Programmable Chip) architectures for efficient embedding of vison-based localization and obstacle detection tasks in a navigational pipeline by making use of the software/hardware co-design methodology. The obtained results are equivalent or better in comparison to state-of-the-art for both EKF-SLAM based visual odometry: regarding the local map size management containing seven-dimensional landmarks and model-based detection-by-identification obstacle detection: algorithmic precision over execution speed metric.
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Submitted on : Friday, February 16, 2018 - 4:27:05 PM
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  • HAL Id : tel-01477662, version 2


Daniel Tortei. Co-design of architectures and algorithms for mobile robot localization and model-based detection of obstacles. Embedded Systems. Université Paul Sabatier - Toulouse III, 2016. English. ⟨NNT : 2016TOU30294⟩. ⟨tel-01477662v2⟩



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