Visual localization within LIDAR maps for automated urban driving

Wolcott, Ryan W.; Eustice, Ryan M.

doi:10.1109/iros.2014.6942558

Cited by 302 publications

(149 citation statements)

References 21 publications

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“…Of course, this aspect is also tightly linked to the cost of those sensors. An interesting perspective in that sense is multi-modality where a map could be built by an expensive 3D laser but then exploited by a simple camera as proposed in [273]. Another interesting challenge is to build flexible architectures in which a decision system can choose what are the sensors, detectors or maps to favor depending on the context.…”

Section: Discussionmentioning

confidence: 99%

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

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

627

358

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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: Discussionmentioning

confidence: 99%

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

Bresson

Alsayed

et al. 2017

IEEE Trans. Intell. Veh.

627

358

View full text Add to dashboard Cite

show abstract

“…This evaluation method is used due to the accuracy of laser sensors. Research in [21,22,23] introduced the use of laser scanning to solve the problem of visual localization within LiDAR maps, extraction of road markings and lane detection respectively. To this end, Table 3 shows the computed RMSE value between the laser and camera.…”

Section: Methodsmentioning

confidence: 99%

Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

Hernández

Kurnianggoro

Filonenko

et al. 2016

Sensors

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Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performance.

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“…To help aid the SA process, FA car prototypes currently under development typically use detailed databases of the road environment to reduce the need for and guide the perception 13 . Far awareness, at least awareness of what static features to expect, is largely obtained from stored data.…”

Section: B Maneuver Loop Comparisonmentioning

confidence: 99%

Transformational Autonomy and Personal Transportation: Synergies and Differences between Cars and Planes

Goodrich

Nickolaou

Moore

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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Highly automated cars have undergone tremendous investment and progress over the past ten years with speculation about fully-driverless cars within the foreseeable, or even near future, becoming common. If a driverless future is realized, what might be the impact on personal aviation? Would self-piloting airplanes be a relatively simple spin-off, possibly making travel by personal aircraft also commonplace? What if the technology for completely removing human drivers turns out to be further in the future rather than sooner; would such a delay suggest that transformational personal aviation is also somewhere over the horizon or can transformation be achieved with less than full automation? This paper presents a preliminary exploration of these questions by comparing the operational, functional, and implementation requirements and constraints of cars and small aircraft for on-demand mobility. In general, we predict that the mission management and perception requirements of self-piloting aircraft differ significantly from self-driving cars and requires the development of aviation specific technologies. We also predict that the highly-reliable control and system automation technology developed for conditionally and highly automated cars can have a significant beneficial effect on personal aviation, even if full automation is not immediately feasible.

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Visual localization within LIDAR maps for automated urban driving

Cited by 302 publications

References 21 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

Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

Transformational Autonomy and Personal Transportation: Synergies and Differences between Cars and Planes

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