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

Xiao, Zhongyang; Yang, Diange; Wen, Tuopu; Jiang, Kun; Yan, Ruidong

doi:10.3390/s20071870

Cited by 31 publications

(9 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast, the traffic landmarks in the HD map are represented in the form of a reference point. Since the reference points are mainly used for HD map-based localization [34], it is beneficial to directly detect traffic landmarks in the form of reference point. Otherwise, additional post-processing is required to find the image coordinate corresponding to the reference point of the detected landmark [35].…”

Section: B Training Traffic Landmark Reference Point Detectionmentioning

confidence: 99%

Semi-Automatic Framework for Traffic Landmark Annotation

Lee

Jung

Kang

et al. 2021

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

We present a semi-automatic annotation method to build a large dataset for traffic landmark detection, where traffic landmarks include traffic signs, traffic lights as well as road markings. Labor-intensive bounding box tagging is a huge challenge to generate a large dataset for detection algorithms. To mitigate the labor, we adopt a high-definition (HD) map and a positioning system. We propose a process to align the HD map and images semi-automatically. Through the registration, the annotations of the HD map can be directly tagged onto traffic landmarks in the images. To make full use of the HD map for the dataset generation, we annotate the traffic landmarks with reference points, following the way that they are represented in the HD map, instead of the bounding boxes. The proposed semi-automatic method speeds up the annotation by a factor of 3.19, as compared to the manual annotation. Our dataset consists of about 150,000 images and includes about 470,000 annotated traffic landmarks. We train a deep neural network on our dataset to detect the traffic landmarks, and its performance is evaluated using a novel evaluation metric. Moreover, we show that the pretrained traffic landmark detection network is effective in detecting traffic landmarks in other countries using the bounding box by fine-tuning.

show abstract

Section: B Training Traffic Landmark Reference Point Detectionmentioning

confidence: 99%

Semi-Automatic Framework for Traffic Landmark Annotation

Lee

Jung

Kang

et al. 2021

IEEE Open J. Intell. Transp. Syst.

View full text Add to dashboard Cite

show abstract

“…Compare to geometry features, semantics representation are alternative and widely used for localization in autonomous driving. Such representations include lane marking, curb, pole and so on [7], [19]- [21].…”

Section: Related Workmentioning

confidence: 99%

“…HD maps are highly structured, organized as entities with geometry and attributes. Several works explore the localization method based on HDMap [3], [4], [7]. However, data association is error prone or the localization system is not complete.…”

Section: Introductionmentioning

confidence: 99%

Coarse-to-fine Semantic Localization with HD Map for Autonomous Driving in Structural Scenes

Guo¹,

Lin²,

Guo³

et al. 2021

Preprint

View full text Add to dashboard Cite

Robust and accurate localization is an essential component for robotic navigation and autonomous driving. The use of cameras for localization with high definition map (HD Map) provides an affordable localization sensor set. Existing methods suffer from pose estimation failure due to error prone data association or initialization with accurate initial pose requirement. In this paper, we propose a cost-effective vehicle localization system with HD map for autonomous driving that uses cameras as primary sensors. To this end, we formulate vision-based localization as a data association problem that maps visual semantics to landmarks in HD map. Specifically, system initialization is finished in a coarse to fine manner by combining coarse GPS (Global Positioning System) measurement and fine pose searching. In tracking stage, vehicle pose is refined by implicitly aligning the semantic segmentation result between image and landmarks in HD maps with photometric consistency. Finally, vehicle pose is computed by pose graph optimization in a sliding window fashion. We evaluate our method on two datasets and demonstrate that the proposed approach yields promising localization results in different driving scenarios. Additionally, our approach is suitable for both monocular camera and multi-cameras that provides flexibility and improves robustness for the localization system.

show abstract

“…For vision sensors, feature-based spatial representation methods such as the vector map are usually established which take less memory but more computational cost than the former. Compared with LIDAR and camera, radar-based localization algorithms are less popular because data semantic features provided by radar are not obvious and the point cloud is relatively sparse [ 82 ]. Nevertheless, recent research works begin to attach importance to radar-based vehicle self-localization [ 83 , 84 ].…”

Section: Mmw Radar Perception Approachesmentioning

confidence: 99%

MMW Radar-Based Technologies in Autonomous Driving: A Review

Zhou

Yang

Jiang

et al. 2020

Sensors

Self Cite

View full text Add to dashboard Cite

With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.

show abstract

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

Cited by 31 publications

References 39 publications

Semi-Automatic Framework for Traffic Landmark Annotation

Semi-Automatic Framework for Traffic Landmark Annotation

Coarse-to-fine Semantic Localization with HD Map for Autonomous Driving in Structural Scenes

MMW Radar-Based Technologies in Autonomous Driving: A Review

Contact Info

Product

Resources

About