Lane-Level Localization Using an AVM Camera for an Automated Driving Vehicle in Urban Environments

Kim, Dong-Wook; Kim, Beom-Jun; Chung, Tae-Young; Yi, Kyongsu

doi:10.1109/tmech.2016.2533635

Cited by 55 publications

(34 citation statements)

References 32 publications

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“…ICP is a widely-used registration method. In some studies [24,36], the map is projected into the camera coordinates based on the camera position, and the matching relationship is estimated iteratively by updating the camera position, assuming that the nearest features are matching pairs. The method nevertheless inevitably introduces the influence of outliers, thus is almost always paired up with the RANSAC method in order to eliminate mismatching and improve accuracy [5,6].…”

Section: Data Association and Vehicle Pose Estimationmentioning

confidence: 99%

“…Based on the association of features between maps and the camera, the vehicle pose can be calculated in the map coordinates. Some preliminary methods simplifies the camera motions to the two-dimensional coordinate system models [34,36], making it unsuitable for scenarios where the external parameters (e.g., elevation, height) of the camera change. A much more accurate camera pose description is the six-DOF model, which is adopted in our work and others [6].…”

Section: Data Association and Vehicle Pose Estimationmentioning

confidence: 99%

“…As for the vehicle localization, two main methods are used in localization parameter estimations: filtering and optimization. Some common filtering methods are the Kalman Filter [3], the Extended Kalman Filter [36] and the particle filter [33,37]-Monte Carlo localization algorithm-which models the probability of sensor readings in order to predict and update the camera pose. In the works pertaining to optimization method [24], the pose estimation problem is viewed as a maximum likelihood (ML) formula where the sensor readings are compared with the map re-projection results to produce constraints, against which the camera pose is estimated iteratively.…”

Section: Data Association and Vehicle Pose Estimationmentioning

confidence: 99%

See 2 more Smart Citations

Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs

Xiao

Yang

Wen

et al. 2020

Sensors

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Real-time vehicle localization (i.e., position and orientation estimation in the world coordinate system) with high accuracy is the fundamental function of an intelligent vehicle (IV) system. In the process of commercialization of IVs, many car manufacturers attempt to avoid high-cost sensor systems (e.g., RTK GNSS and LiDAR) in favor of low-cost optical sensors such as cameras. The same cost-saving strategy also gives rise to an increasing number of vehicles equipped with High Definition (HD) maps. Rooted upon these existing technologies, this article presents the concept of Monocular Localization with Vector HD Map (MLVHM), a novel camera-based map-matching method that efficiently aligns semantic-level geometric features in-camera acquired frames against the vector HD map in order to achieve high-precision vehicle absolute localization with minimal cost. The semantic features are delicately chosen for the ease of map vector alignment as well as for the resiliency against occlusion and fluctuation in illumination. The effective data association method in MLVHM serves as the basis for the camera position estimation by minimizing feature re-projection errors, and the frame-to-frame motion fusion is further introduced for reliable localization results. Experiments have shown that MLVHM can achieve high-precision vehicle localization with an RMSE of 24 cm with no cumulative error. In addition, we use low-cost on-board sensors and light-weight HD maps to achieve or even exceed the accuracy of existing map-matching algorithms.

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Section: Data Association and Vehicle Pose Estimationmentioning

confidence: 99%

Section: Data Association and Vehicle Pose Estimationmentioning

confidence: 99%

Section: Data Association and Vehicle Pose Estimationmentioning

confidence: 99%

See 1 more Smart Citation

Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs

Xiao

Yang

Wen

et al. 2020

Sensors

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show abstract

“…With the rapid development of digital maps, navigation algorithms based on map matching have also been extensively studied [6], [7], [9], [10]. Map matching algorithms match the noisy GNSS positioning results to a trajectory that satisfies known road geometry constraints.…”

Section: Related Workmentioning

confidence: 99%

“…Map matching algorithms match the noisy GNSS positioning results to a trajectory that satisfies known road geometry constraints. Additional sensors such as cameras or lidars in combination with high-definition maps can reduce localization error down to the centimeter-level [10]. This approach requires both accurate sensors and accurate maps.…”

Section: Related Workmentioning

confidence: 99%

Improving Localization Accuracy in Connected Vehicle Networks Using Rao–Blackwellized Particle Filters: Theory, Simulations, and Experiments

Shen

Sun

Peng

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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A crucial function for automated vehicle technologies is accurate localization. Lane-level accuracy is not readily available from low-cost Global Navigation Satellite System (GNSS) receivers because of factors such as multipath error and atmospheric bias. Approaches such as Differential GNSS can improve localization accuracy, but usually require investment in expensive base stations. Connected vehicle technologies provide an alternative approach to improving the localization accuracy. It will be shown in this paper that localization accuracy can be enhanced using crude GNSS measurements from a group of connected vehicles, by matching their locations to a digital map. A Rao-Blackwellized particle filter (RBPF) is used to jointly estimate the common biases of the pseudo-ranges and the vehicle positions. Multipath biases, which introduce receiverspecific (non-common) error, are mitigated by a multi-hypothesis detection-rejection approach. The temporal correlation of the estimations is exploited through the prediction-update process. The proposed approach is compared to existing methods using both simulations and experimental results. It was found that the proposed algorithm can eliminate the common biases and reduce the localization error to below 1 meter under open sky conditions.

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Lane Detection

Ren

Dong²

2023

Autonomous Driving Algorithms and Its IC Design

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Lane-Level Localization Using an AVM Camera for an Automated Driving Vehicle in Urban Environments

Cited by 55 publications

References 32 publications

Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs

Monocular Localization with Vector HD Map (MLVHM): A Low-Cost Method for Commercial IVs

Improving Localization Accuracy in Connected Vehicle Networks Using Rao–Blackwellized Particle Filters: Theory, Simulations, and Experiments

Lane Detection

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