An online probabilistic road intersection detector

Ballardini, Augusto Luis; Cattaneo, Daniele; Fontana, Simone; Sorrenti, Domenico G.

doi:10.1109/icra.2017.7989030

Cited by 15 publications

(19 citation statements)

References 38 publications

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“…Although commercial solutions commonly exploit GNSSs receivers in combination with Inertial navigation system(INSs) and digital maps, such as in Siemens’s navigation system [ 16 ], nowadays the research community proposes to exploit the large set of scene elements that can be found in typical driving scenarios. These scene elements could be, e.g., road markings [ 17 ], traffic lights [ 18 ], or even roundabouts and intersections [ 19 , 20 , 21 , 22 ]. From a technical perspective, the existing localization approaches can be divided into two main categories.…”

Section: Related Workmentioning

confidence: 99%

“…If the perceived buildings are well aligned with the data on OSM, then the particle represents a good estimate vehicle’s pose and, therefore, should be given a high score. Most importantly, this LC, and thus the proposed approach, should not be used alone, but it should be combined with others [ 20 ] to improve the robustness of the approach, e.g., when no building is clearly visible.…”

Section: Proposed Localization Pipelinementioning

confidence: 99%

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Vehicle Localization Using 3D Building Models and Point Cloud Matching

Ballardini

Fontana

Cattaneo

et al. 2021

Sensors

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Detecting buildings in the surroundings of an urban vehicle and matching them to building models available on map services is an emerging trend in robotics localization for urban vehicles. In this paper, we present a novel technique, which improves a previous work by detecting building façade, their positions, and finding the correspondences with their 3D models, available in OpenStreetMap. The proposed technique uses segmented point clouds produced using stereo images, processed by a convolutional neural network. The point clouds of the façades are then matched against a reference point cloud, produced extruding the buildings’ outlines, which are available on OpenStreetMap (OSM). In order to produce a lane-level localization of the vehicle, the resulting information is then fed into our probabilistic framework, called Road Layout Estimation (RLE). We prove the effectiveness of this proposal, testing it on sequences from the well-known KITTI dataset and comparing the results concerning a basic RLE version without the proposed pipeline.

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Section: Related Workmentioning

confidence: 99%

Section: Proposed Localization Pipelinementioning

confidence: 99%

Vehicle Localization Using 3D Building Models and Point Cloud Matching

Ballardini

Fontana

Cattaneo

et al. 2021

Sensors

Self Cite

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“…Another class of approaches to autonomous driving uses topometric maps (predominantly OpenStreetMaps [25]). In a series of works, Ballardini et al [26]- [28] leverage the OSM for localization and intersection detection. In a similar vein, OpenStreetSLAM [29] aligns OSM data to local maps as an additional cue in a Monte-Carlo localization framework.…”

Section: Related Workmentioning

confidence: 99%

MapLite: Autonomous Intersection Navigation Without a Detailed Prior Map

Ort

Murthy

Banerjee

et al. 2020

IEEE Robot. Autom. Lett.

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In this work, we present MapLite: a one-click autonomous navigation system capable of piloting a vehicle to an arbitrary desired destination point given only a sparse publicly available topometric map (from OpenStreetMap). The onboard sensors are used to segment the road region and register the topometric map in order to fuse the high-level navigation goals with a variational path planner in the vehicle frame. This enables the system to plan trajectories that correctly navigate road intersections without the use of an external localization system such as GPS or a detailed prior map. Since the topometric maps already exist for the vast majority of roads, this solution greatly increases the geographical scope for autonomous mobility solutions. We implement MapLite on a full-scale autonomous vehicle and exhaustively test it on over 15 km of road including over 100 autonomous intersection traversals. We further extend these results through simulated testing to validate the system on complex road junction topologies such as traffic circles.

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“…However, images as well as their interpretations (i.e. segmented pixels) in this perspective are often transformed into a local and/or global coordinate system (or view) to be utilized effectively within tasks such as lane detection [3], [4], road marking detection [5], road topology detection [6], [7], object detection/tracking [8]- [10], as well as path planning and intersection prediction [11], [12]. This transformation is commonly referred to as Inverse Perspective Mapping (IPM) [13].…”

Section: Introductionmentioning

confidence: 99%

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

Bruls

Porav

Kunze

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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Many tasks performed by autonomous vehicles such as road marking detection, object tracking, and path planning are simpler in bird's-eye view. Hence, Inverse Perspective Mapping (IPM) is often applied to remove the perspective effect from a vehicle's front-facing camera and to remap its images into a 2D domain, resulting in a top-down view. Unfortunately, however, this leads to unnatural blurring and stretching of objects at further distance, due to the resolution of the camera, limiting applicability. In this paper, we present an adversarial learning approach for generating a significantly improved IPM from a single camera image in real time. The generated bird'seye-view images contain sharper features (e.g. road markings) and a more homogeneous illumination, while (dynamic) objects are automatically removed from the scene, thus revealing the underlying road layout in an improved fashion. We demonstrate our framework using real-world data from the Oxford Robot-Car Dataset and show that scene understanding tasks directly benefit from our boosted IPM approach.

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An online probabilistic road intersection detector

Cited by 15 publications

References 38 publications

Vehicle Localization Using 3D Building Models and Point Cloud Matching

Vehicle Localization Using 3D Building Models and Point Cloud Matching

MapLite: Autonomous Intersection Navigation Without a Detailed Prior Map

The Right (Angled) Perspective: Improving the Understanding of Road Scenes Using Boosted Inverse Perspective Mapping

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