A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving

Qin, Tong; Zheng, Yuxin; Chen, Tongqing; Chen, Yilun; Su, Qing

doi:10.1109/icra48506.2021.9561663

Cited by 51 publications

(23 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CenterNet network is used to detect road semantic features, key points represent lane lines and road signs, and semantic associations are used to optimize the overall state. Tong Qin et al [28] developed a lightweight autonomous driving positioning framework that included vehicle-side mapping, cloud-based maintenance, and user-side positioning. Learning-based semantic segmentation is used to extract significant landmarks.…”

Section: Related Wordmentioning

confidence: 99%

Accurate Location in Dynamic Traffic Environment Using Semantic Information and Probabilistic Data Association

Yang

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

High-accurate and real-time localization is the fundamental and challenging task for autonomous driving in a dynamic traffic environment. This paper presents a coordinated positioning strategy that is composed of semantic information and probabilistic data association, which improves the accuracy of SLAM in dynamic traffic settings. First, the improved semantic segmentation network, building on Fast-SCNN, uses the Res2net module instead of the Bottleneck in the global feature extraction to further explore the multi-scale granular features. It achieves the balance between segmentation accuracy and inference speed, leading to consistent performance gains on the coordinated localization task of this paper. Second, a novel scene descriptor combining geometric, semantic, and distributional information is proposed. These descriptors are made up of significant features and their surroundings, which may be unique to a traffic scene, and are used to improve data association quality. Finally, a probabilistic data association is created to find the best estimate using a maximum measurement expectation model. This approach assigns semantic labels to landmarks observed in the environment and is used to correct false negatives in data association. We have evaluated our system with ORB-SLAM2 and DynaSLAM, the most advanced algorithms, to demonstrate its advantages. On the KITTI dataset, the results reveal that our approach outperforms other methods in dynamic traffic situations, especially in highly dynamic scenes, with sub-meter average accuracy.

show abstract

Section: Related Wordmentioning

confidence: 99%

Accurate Location in Dynamic Traffic Environment Using Semantic Information and Probabilistic Data Association

Yang

Zhang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…To make the best of the fusion mechanism in [7], apart from camera and LiDAR data, an OpenDrive HD map and radar data are added as the network inputs in the proposed approach. Even though the HD map in this work is obtained from the CARLA simulator, there are still several approaches to export the HD map automatically [25], [26]. Additionally, to improve the model's adaptability to dynamic environments, long-range radar is used.…”

Section: B Input and Output Representationmentioning

confidence: 99%

MMFN: Multi-Modal-Fusion-Net for End-to-End Driving

Zhang¹,

Tang²,

Geng³

et al. 2022

Preprint

View full text Add to dashboard Cite

Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused through transformers for better driving performance. These inputs are normally further interpreted as high-level map information to assist navigation tasks. Nevertheless, extracting useful information from the complex map input is challenging, for redundant information may mislead the agent and negatively affect driving performance. We propose a novel approach to efficiently extract features from vectorized High-Definition (HD) maps and utilize them in the end-to-end driving tasks. In addition, we design a new expert to further enhance the model performance by considering multi-road rules. Experimental results prove that both of the proposed improvements enable our agent to achieve superior performance compared with other methods. The source code is released as an open-source package.• We use a multi-path consideration rule-based expert to

show abstract

“…Also, Figure 5 illustrates the layers of HD map defined by HERE [96]. In Lyft's HD map, the five core layers are described as follows [97,98]: • Semantic map layer: The semantic map layer contains all semantic data, such as lane marker placements, travel directions, and traffic sign locations [21,99,100]. Within the semantic layer, there are three major sub-layers:…”

Section: High Definition (Hd) Mapsmentioning

confidence: 99%

High-Definition Map Representation Techniques for Automated Vehicles

Soorchaei¹,

Razzaghpour²,

Romero³

et al. 2022

Preprint

View full text Add to dashboard Cite

Many studies in the field of robot navigation have focused on environment representation and localization. The goal of map representation is to summarize spatial information in topological and geometrical abstracts. By providing strong priors, maps improve the performance and reliability of automated robots. Due to the transition to fully automated driving in recent years, there has been a constant effort to design methods and technologies to improve the precision of road participants and the environment's information. Among these efforts is the High Definition (HD) Map concept. Making HD maps requires accuracy, completeness, verifiability, and extensibility. Because of the complexity of HD mapping, it is currently expensive and difficult to implement, particularly in an urban environment. In an urban traffic system, the road model is at least a map with sets of roads, lanes, and lane markers. While more research is being dedicated to mapping and localization, a comprehensive review of the various types of map representation is still required. This paper presents a brief overview of map representation, followed by a detailed literature review of HD Map for automated vehicles. The current state of AV mapping is encouraging, the field has matured to a point where detailed maps of complex environments are built in real-time and have been proved useful. Many existing techniques are robust to noise and can cope with a large range of environments. Nevertheless, there are still open problems for future research. AV mapping will continue to be a highly active research area essential to the goal of achieving full autonomy.

show abstract

A Light-Weight Semantic Map for Visual Localization towards Autonomous Driving

Cited by 51 publications

References 23 publications

Accurate Location in Dynamic Traffic Environment Using Semantic Information and Probabilistic Data Association

Accurate Location in Dynamic Traffic Environment Using Semantic Information and Probabilistic Data Association

MMFN: Multi-Modal-Fusion-Net for End-to-End Driving

High-Definition Map Representation Techniques for Automated Vehicles

Contact Info

Product

Resources

About