Feature-Based 6-DoF Camera Localization Using Prior Point Cloud and Images

Kim, Hyongjin; Lee, Donghwa; Oh, Taekjun; Lee, Sang Won; Choe, Yoonsik; Myung, Hyun

doi:10.1007/978-3-319-05582-4_1

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…Meanwhile, according to the research achievements in Ref. [18] we investigate the feasibility of receiving the bearing state by machine vision. In order to reduce the onboard loads and to ensure the safe distance of the quadrotor systems, we introduce a hierarchical network approach and design protocols for masters and slaves respectively.…”

Section: Introductionmentioning

confidence: 99%

Hunting problems of multi-quadrotor systems via bearing-based hybrid protocols with hierarchical network*

Xu¹,

Liu²,

Chen³

et al. 2020

Chinese Phys. B

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Bearing-based hunting protocols commonly adopt a leaderless consensus method, which requests an entire state of the target for each agent and ignores the necessity of collision avoidance. We investigate a hunting problem of multi-quadrotor systems with hybrid bearing protocols, where the quadrotor systems are divided into master and slave groups for reducing the onboard loads and collision avoidance. The masters obtain the entire state of the target, whose hybrid protocols are based on the displacement and bearing constraints to maintain formation and to avoid the collision in the hunting process. However, the slaves’ protocols merely depend on the part state of the masters to reduce loads of data transmission. We also investigate the feasibility of receiving the bearing state from machine vision. The simulation results are given to illustrate the effectiveness of the proposed hybrid bearing protocols.

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Section: Introductionmentioning

confidence: 99%

Hunting problems of multi-quadrotor systems via bearing-based hybrid protocols with hierarchical network*

Xu¹,

Liu²,

Chen³

et al. 2020

Chinese Phys. B

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show abstract

“…[ 9 ] proposed an image-based localization method in urban environments using triangulation of matched features from database images. 3D feature map-based localization methods are introduced by matching features between a query image and the 3D map which is generated from image databases [ 10 , 11 ]. The accuracy of the feature map is important in feature-based localization methods because errors of the map directly influence the accuracy of localization.…”

Section: Introductionmentioning

confidence: 99%

“…It therefore remains challenging to apply the feature-based localization methods with insufficient and uncertain features. Since the most feature-based localization methods [ 3 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ] use camera pose estimation methods [ 20 , 21 , 22 , 23 , 24 ], uncertainty of features is not considered during the camera pose estimation. color1Although Ferraz et al .…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Feature Map-Based Localization System Using a Monocular Camera

Kim

Lee

et al. 2015

Sensors

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Image-based localization is one of the most widely researched localization techniques in the robotics and computer vision communities. As enormous image data sets are provided through the Internet, many studies on estimating a location with a pre-built image-based 3D map have been conducted. Most research groups use numerous image data sets that contain sufficient features. In contrast, this paper focuses on image-based localization in the case of insufficient images and features. A more accurate localization method is proposed based on a probabilistic map using 3D-to-2D matching correspondences between a map and a query image. The probabilistic feature map is generated in advance by probabilistic modeling of the sensor system as well as the uncertainties of camera poses. Using the conventional PnP algorithm, an initial camera pose is estimated on the probabilistic feature map. The proposed algorithm is optimized from the initial pose by minimizing Mahalanobis distance errors between features from the query image and the map to improve accuracy. To verify that the localization accuracy is improved, the proposed algorithm is compared with the conventional algorithm in a simulation and realenvironments.

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Multimodal localization: Stereo over LiDAR map

Zuo

Yang

et al. 2020

Journal of Field Robotics

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In this paper, we present a real‐time high‐precision visual localization system for an autonomous vehicle which employs only low‐cost stereo cameras to localize the vehicle with a priori map built using a more expensive 3D LiDAR sensor. To this end, we construct two different visual maps: a sparse feature visual map for visual odometry (VO) based motion tracking, and a semidense visual map for registration with the prior LiDAR map. To register two point clouds sourced from different modalities (i.e., cameras and LiDAR), we leverage probabilistic weighted normal distributions transformation (ProW‐NDT), by particularly taking into account the uncertainty of source point clouds. The registration results are then fused via pose graph optimization to correct the VO drift. Moreover, surfels extracted from the prior LiDAR map are used to refine the sparse 3D visual features that will further improve VO‐based motion estimation. The proposed system has been tested extensively in both simulated and real‐world experiments, showing that robust, high‐precision, real‐time localization can be achieved.

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Feature-Based 6-DoF Camera Localization Using Prior Point Cloud and Images

Cited by 5 publications

References 8 publications

Hunting problems of multi-quadrotor systems via bearing-based hybrid protocols with hierarchical network*

Hunting problems of multi-quadrotor systems via bearing-based hybrid protocols with hierarchical network*

A Probabilistic Feature Map-Based Localization System Using a Monocular Camera

Multimodal localization: Stereo over LiDAR map

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