Localization in Urban Environments: Monocular Vision Compared to a Differential GPS Sensor

Royer, Eric; Lhuillier, Maxime; Dhome, Michel; Chateau, Thierry

doi:10.1109/cvpr.2005.217

Cited by 55 publications

(48 citation statements)

References 14 publications

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“…This property makes it possible to find correct correspondence between two images from different cameras. This feasibility has been shown by [39] in two sequences of images taken by a pair of cameras: one sequence was used in the map learning step, and the other one was used for vehicle localization; the result turns out to be robust.…”

Section: Discussionmentioning

confidence: 99%

“…The paper [39] has an average localization error of 15 cm over 6 localization experiments on 3 outdoor sequences. The reported mean indoor localization errors of [41] are ranged between 5.6 cm and 10.8 cm among 6 experiments.…”

Section: Discussionmentioning

confidence: 99%

“…We bring forward a two-stage framework (mapping and localization) to solve the problem. Closer approaches have already been proposed by different researchers [39][40][41]. They all involve a visual learning step to reconstruct a map of the environment, and they use this map for localization and navigation task.…”

Section: Discussionmentioning

confidence: 99%

“…Eric Royer et al [39] uses bundle adjustment in mapping step and the localization results are obtained via Newton iteration method. Hyon Lim et al [41] uses a structure from motion (SFM) algorithm to reconstruct the 3D environment; then in the localization step, two discrete Kalman filters are employed to estimate the camera trajectory.…”

Section: Discussionmentioning

confidence: 99%

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A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

Wang

Lambert

2018

Journal of Advanced Transportation

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Probabilistic techniques (such as Extended Kalman Filter and Particle Filter) have long been used to solve robotic localization and mapping problem. Despite their good performance in practical applications, they could suffer inconsistency problems. This paper proposes an interval analysis based method to estimate the vehicle pose (position and orientation) in a consistent way, by fusing low-cost sensors and map data. We cast the localization problem into an Interval Constraint Satisfaction Problem (ICSP), solved via Interval Constraint Propagation (ICP) techniques. An interval map is built when a vehicle embedding expensive sensors navigates around the environment. Then vehicles with low-cost sensors (dead reckoning and monocular camera) can use this map for ego-localization. Experimental results show the soundness of the proposed method in achieving consistent localization.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

Wang

Lambert

2018

Journal of Advanced Transportation

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“…This method reaches real-time if the number of landmarks is quite small. Another approach is to use a full non-linear optimisation of the scene geometry: Royer (Royer et al, 2005) uses a hierarchical bundle adjustment in order to build large-scale scenes. Afterwards, Mouragnon (Mouragnon et al, 2006) proposes an incremental non-linear minimisation method in order to almost completely avoid computer memory problem by only optimizing the position of the geometry scene on the few last cameras.…”

Section: Introductionmentioning

confidence: 99%

Toward Large Scale Model Construction for Vision-Based Global Localisation

Lothe

Bourgeois²,

Dekeyser³

et al. 2009

Proceedings of the Fourth International Conference on Computer Vision Theory and Applications

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Abstract:Monocular SLAM reconstruction algorithm advancements enable their integration in various applications: trajectometry, 3D model reconstruction, etc. However proposed methods still have drift limitations when applied to large-scale sequences. In this paper, we propose a post-processing algorithm which exploits a CAD model to correct SLAM reconstructions. The presented method is based on a specific deformable transformations model and then on an adapted non-rigid ICP between the reconstructed 3D point cloud and the known CAD model. Experimental results on both synthetic and real sequences point out that the 3D scene geometry regains its consistency and that the camera trajectory is improved: mean distance between the reconstructed cameras and the ground truth is less than 1 meter on several hundreds of meters.

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Dense Omnidirectional RGB‐D Mapping of Large‐scale Outdoor Environments for Real‐time Localization and Autonomous Navigation

Meilland

Comport

Rives

2014

Journal of Field Robotics

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This paper presents a novel method and innovative apparatus for building three‐dimensional (3D) dense visual maps of large‐scale unstructured environments for autonomous navigation and real‐time localization. The main contribution of the paper is focused on proposing an efficient and accurate 3D world representation that allows us to extend the boundaries of state‐of‐the‐art dense visual mapping to large scales. This is achieved via an omnidirectional key‐frame representation of the environment, which is able to synthesize photorealistic views of captured environments at arbitrary locations. Locally, the representation is image‐based (egocentric) and is composed of accurate augmented spherical panoramas combining photometric information (RGB), depth information (D), and saliency for all viewing directions at a particular point in space (i.e., a point in the light field). The spheres are related by a graph of six degree of freedom (DOF) poses (3 DOF translation and 3 DOF rotation) that are estimated through multiview spherical registration. It is shown that this world representation can be used to perform robust real‐time localization (in 6 DOF) of any configuration of visual sensors within their environment, whether they be monocular, stereo, or multiview. Contrary to feature‐based approaches, an efficient direct image registration technique is formulated. This approach directly exploits the advantages of the spherical representation by minimizing a photometric error between a current image and a reference sphere. Two novel multicamera acquisition systems have been developed and calibrated to acquire this information, and this paper reports for the first time the second system. Given the robustness and efficiency of this representation, field experiments demonstrating autonomous navigation and large‐scale mapping will be reported in detail for challenging unstructured environments, containing vegetation, pedestrians, varying illumination conditions, trams, and dense traffic.

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Localization in Urban Environments: Monocular Vision Compared to a Differential GPS Sensor

Cited by 55 publications

References 14 publications

A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

A Low-Cost Consistent Vehicle Localization Based on Interval Constraint Propagation

Toward Large Scale Model Construction for Vision-Based Global Localisation

Dense Omnidirectional RGB‐D Mapping of Large‐scale Outdoor Environments for Real‐time Localization and Autonomous Navigation

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