Lessons learned after more than 1000 km in an autonomous shuttle guided by vision

Royer, Eric; Marmoiton, F.; Alizon, Serge; Ramadasan, Datta; Slade, Morgan; Nizard, Ange; Dhome, Michel; Thuilot, Benoı̂t; Bonjean, Florent

doi:10.1109/itsc.2016.7795919

Cited by 9 publications

(9 citation statements)

References 13 publications

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“…In [264], the authors discuss the lessons learned after 1,500 km of a vision-guided shuttle in a private site. The experiments took place over 3 months and the localization was performed using a hierarchical Bundle Adjustment method over a previously built map.…”

Section: Vehiclesmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

705

366

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Abstract-In this article, we propose a survey of the Simultaneous Localization And Mapping field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multi-vehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various largescale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

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Section: Vehiclesmentioning

confidence: 99%

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

705

366

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“…Below this threshold, we considered it a localization failure. We have set a conservative threshold based on a study conducted by Royer et al [16] to ensure the safety of the autonomous shuttle. This threshold, which we have adopted, is considered conservative and helps maintain a high level of security during the localization process.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Uniform Distribution of Landmarks for Efficient Map Compression

Bouaziz,

Royer,

Bresson

et al. 2023

Lecture Notes in Computer Science

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Copyright

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“…Proprioception is ensured by three odometric sensors which return the front and rear steering angles at 20 Hz as well as the forward speed measured on the engine shaft. The EZ10 features two cameras (front and rear) used for visual odometry [ 30 ] (only used here as a ground truth when combined with GNSS). A Kalman filter is used to merge these odometric data with data from an RTK-GPS (Real Time Kinematic Global Positioning System), which provides centimeter localization reference truth.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Framework for Optimal Navigation in Situations of Localization Uncertainty

Orou Mousse,

Benrabah,

Marmoiton

et al. 2023

Sensors

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The basic functions of an autonomous vehicle typically involve navigating from one point to another in the world by following a reference path and analyzing the traversability along this path to avoid potential obstacles. What happens when the vehicle is subject to uncertainties in its localization? All its capabilities, whether path following or obstacle avoidance, are affected by this uncertainty, and stopping the vehicle becomes the safest solution. In this work, we propose a framework that optimally combines path following and obstacle avoidance while keeping these two objectives independent, ensuring that the limitations of one do not affect the other. Absolute localization uncertainty only has an impact on path following, and in no way affects obstacle avoidance, which is performed in the robot’s local reference frame. Therefore, it is possible to navigate with or without prior information, without being affected by position uncertainty during obstacle avoidance maneuvers. We conducted tests on an EZ10 shuttle in the PAVIN experimental platform to validate our approach. These experimental results show that our approach achieves satisfactory performance, making it a promising solution for collision-free navigation applications for mobile robots even when localization is not accurate.

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Lessons learned after more than 1000 km in an autonomous shuttle guided by vision

Cited by 9 publications

References 13 publications

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

A Uniform Distribution of Landmarks for Efficient Map Compression

A Framework for Optimal Navigation in Situations of Localization Uncertainty

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