On line mapping and global positioning for autonomous driving in urban environment based on evidential SLAM

Trehard, Guillaume; Pollard, Evangeline; Bradai, Benazouz; Nashashibi, Fawzi

doi:10.1109/ivs.2015.7225785

Cited by 20 publications

(11 citation statements)

References 17 publications

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“…• Take better advantage of the ConflictError value. A possible use is as a threshold in a go/no-go condition on the feasibility of the fusion; • Implement a method of grid alignment, such as the one proposed in [14] and [15], to face the uncertainty on the pose estimations; • Enlarge the frame of discernment replacing the class {O} with {S, D} to consider both static and dynamic cells, and implement a solution able to detect moving cells such as the one proposed in [16].…”

Section: Discussionmentioning

confidence: 99%

Fusion of evidential occupancy grids for cooperative perception

Camarda

Davoine

Cherfaoui

2018

2018 13th Annual Conference on System of Systems Engineering (SoSE)

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In the field of autonomous navigation, perception of the driving scene is one of the essential elements. Existing solutions combine on-board exteroceptive sensors, and are capable of understanding some of the near vehicle's dynamic surrounding environment. Furthermore, the perception capability of each vehicle can be enhanced by wireless information sharing if vehicles in the neighborhood transmit pertinent information. The primary benefit of such approach is to enable and improve the performance-safety of cooperative autonomous driving. The introduction of such a vehicle-to-vehicle communication leads to think of the global architecture as a system of systems. In this work, we address the task of evidential occupancy grid fusion so that a given vehicle can refine and complete its occupancy grid with the help of grids received from other near vehicles. The communication channel is supposed to be ideal, noiseless with infinite capacity. We focus on the fusion framework itself, using the theory of belief functions for reasoning with uncertainties on the relative poses of the vehicles and on the exchanged sensor measurement data. We evaluate the fusion system with real data acquired on public roads, with two connected vehicles.

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Section: Discussionmentioning

confidence: 99%

Fusion of evidential occupancy grids for cooperative perception

Camarda

Davoine

Cherfaoui

2018

2018 13th Annual Conference on System of Systems Engineering (SoSE)

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“…The output of the do-not-enter sign detector is a binary variable, z DNE , giving a value of 1 when a do-not-enter sign is detected and 0 when one is not. The output of the one-way sign detector are binary variables representing the detection of left or right one-way signs, as shown in the following in Equation 11:…”

Section: One-way and Do-not-enter Sign Detectionmentioning

confidence: 99%

“…However, this method also requires time‐consuming data collection with heavy data postprocessing. The GPS‐fused simultaneous localization and mapping (SLAM) techniques have been proposed to reduce the complexity of the mapping task . However, the assumption of consistently receiving these GPS measurement updates is not valid for urban applications, such as in urban canyons like Manhattan and Chicago.…”

Section: Introductionmentioning

confidence: 99%

“…The GPS-fused simultaneous localization and mapping (SLAM) techniques have been proposed to reduce the complexity of the mapping task. 11,12 However, the assumption of consistently receiving these GPS measurement updates is not valid for urban applications, such as in urban canyons like Manhattan and Chicago. Therefore, a localization solution that relies upon consistent accurate GPS measurements will ultimately prove to be unreliable.…”

Section: Introductionmentioning

confidence: 99%

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Estimation and navigation methods with limited information for autonomous urban driving

Chipka

Campbell

2019

Engineering Reports

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Autonomous driving in dense urban areas presents an especially difficult task. First, globally localizing information, such as GPS signal, often proves to be unreliable in such areas due to signal shadowing and multipath errors. Second, the high‐definition environmental maps with sufficient information for autonomous navigation require a large amount of data to be collected from these areas, significant postprocessing of this data to generate the map, and then continual maintenance of the map to account for changes in the environment. This paper addresses the issue of autonomous driving in urban environments by investigating algorithms and an architecture to enable fully functional autonomous driving with little to no reliance on map‐based measurements or GPS signals. An extended Kalman filter with odometry, compass, and sparse landmark measurements as inputs is used to provide localization. Real‐time detection and estimation of key roadway features are used to create an understanding of the surrounding static scene. Navigation is accomplished by a compass‐based navigation control law. Experimental scene understanding results are obtained using computer vision and estimation techniques and demonstrate the ability to probabilistically infer key features of an intersection in real time. Key results from Monte Carlo studies demonstrate the proposed localization and navigation methods. These tests provide success rates of urban navigation under different environmental conditions, such as landmark density, and show that the vehicle can navigate to a goal nearly 10 km away without any external pose update at all. Field tests validate these simulated results and demonstrate that, for given test conditions, an expected range can be determined for a given success rate.

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“…This algorithm retained a converged and accurate position estimate during an 8-minute GPS blackout. To address autonomous navigation without a priori map data, GPS-fused SLAM techniques have also been proposed [15], [16]. However, the assumption of consistently receiving these GPS measurement updates is not valid for urban applications, such as in urban canyons like Manhattan and Chicago, and therefore should not be relied upon.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Urban Localization and Navigation with Limited Information

Chipka

Campbell

2018

2018 IEEE Intelligent Vehicles Symposium (IV)

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Urban environments offer a challenging scenario for autonomous driving. Globally localizing information, such as a GPS signal, can be unreliable due to signal shadowing and multipath errors. Detailed a priori maps of the environment with sufficient information for autonomous navigation typically require driving the area multiple times to collect large amounts of data, substantial post-processing on that data to obtain the map, and then maintaining updates on the map as the environment changes. This paper addresses the issue of autonomous driving in an urban environment by investigating algorithms and an architecture to enable fully functional autonomous driving with limited information. An algorithm to autonomously navigate urban roadways with little to no reliance on an a priori map or GPS is developed. Localization is performed with an extended Kalman filter with odometry, compass, and sparse landmark measurement updates. Navigation is accomplished by a compass-based navigation control law. Key results from Monte Carlo studies show success rates of urban navigation under different environmental conditions. Experiments validate the simulated results and demonstrate that, for given test conditions, an expected range can be found for a given success rate.

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On line mapping and global positioning for autonomous driving in urban environment based on evidential SLAM

Cited by 20 publications

References 17 publications

Fusion of evidential occupancy grids for cooperative perception

Fusion of evidential occupancy grids for cooperative perception

Estimation and navigation methods with limited information for autonomous urban driving

Autonomous Urban Localization and Navigation with Limited Information

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