Multi-sensor Fusion Towards VINS: A Concise Tutorial, Survey, Framework and Challenges

Dinh, Nam Van; Kim, Gon–Woo

doi:10.1109/bigcomp48618.2020.00-26

Cited by 12 publications

(5 citation statements)

References 27 publications

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“…The distance between the Head-Tp and Tail-Tp of RNBEcur is Len TPs . If the above variables satisfy Equation (7), it means that the vehicle is passing through the road intersection for the first time. Another condition is if the variables satisfies Equation (8), which means that the vehicle has not passed the road intersection at the current moment, but the vehicle will pass the road intersection at the next moment and be closer to T p than this moment.…”

Section: ( ) ( )mentioning

confidence: 99%

“…However, due to the inherent shortcomings of satellite positioning principles, the global satellite positioning system has problems, such as unstable signals and low local positioning accuracy in urban canyon environments [3,4]. Inertial sensor [5][6][7] or wheel odometry [8][9][10] data are usually introduced into the positioning system to provide accurate and robust vehicle positioning information. But the external parameters between different sensors in the multi-sensor fusion system are easily affected by mechanical deformation and harsh environments, which will lead to a reduction in system positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization

Xu,

Sun,

Zhao

et al. 2023

Sensors

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Satellite signals are easily lost in urban areas, which causes difficulty in vehicles being located with high precision. Visual odometry has been increasingly applied in navigation systems to solve this problem. However, visual odometry relies on dead-reckoning technology, where a slight positioning error can accumulate over time, resulting in a catastrophic positioning error. Thus, this paper proposes a road-network-map-assisted vehicle positioning method based on the theory of pose graph optimization. This method takes the dead-reckoning result of visual odometry as the input and introduces constraints from the point-line form road network map to suppress the accumulated error and improve vehicle positioning accuracy. We design an optimization and prediction model, and the original trajectory of visual odometry is optimized to obtain the corrected trajectory by introducing constraints from map correction points. The vehicle positioning result at the next moment is predicted based on the latest output of the visual odometry and corrected trajectory. The experiments carried out on the KITTI and campus datasets demonstrate the superiority of the proposed method, which can provide stable and accurate vehicle position estimation in real-time, and has higher positioning accuracy than similar map-assisted methods.

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Section: ( ) ( )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization

Xu,

Sun,

Zhao

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In literature, there are a great number of reviews, surveys and summaries [1]- [40] on localization algorithms and systems. Due to the fast development of the field, there is an urgent need for an updated survey paper that includes the latest works and the future trend.…”

Section: A Existing Survey Papersmentioning

confidence: 99%

“…The most recent works [31]- [40] concentrated on sensor fusion based localization study. Most of the latest fusion studies are based on an autonomous car application that requires high precision and robust localization results.…”

Section: A Existing Survey Papersmentioning

confidence: 99%

Survey on Localization Systems and Algorithms for Unmanned Systems

2021

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Intelligent unmanned systems have important applications, such as pesticide-spraying in agriculture, robot-based warehouse management systems, and missile-firing drones. The underlying assumption behind all autonomy is that the agent knows its relative position or egomotion with respect to some reference or scene. There exist thousands of localization systems in the literature. These localization systems use various combinations of sensors and algorithms, such as visual/visual-inertial SLAM, to achieve robust localization. The majority of the methods use one or more sensors from LIDAR, camera, IMU, UWB, GPS, compass, tracking system, etc. This survey presents a systematic review and analysis of published algorithms and techniques chronologically, and we introduce various highly impactful works. We provide insightful investigation and taxonomy on sensory data forming principle, feature association principle, egomotion estimation formation, and fusion model for each type of system. At last, some open problems and directions for future research are also included. We aim to survey the literature comprehensively to provide a complete understanding of localization methodologies, performance, advantages and limitations, and evaluations of various methods, shedding some light for future research.

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“…Recently, several surveys about multi-sensor fusion SALM have been proposed. Some reviews [4][5][6][7] focus on multi-sensor fusion in autonomous driving, while most reviews [8][9][10][11][12][13] pay attention to visual-inertial SLAM. There are few surveys about LiDAR-inertial or visual-LiDAR SLAM [14] .…”

Section: Introductionmentioning

confidence: 99%

Camera, LiDAR, and IMU Based Multi-Sensor Fusion SLAM: A Survey

Zhu,

Li,

Zhang

2024

Tsinghua Sci. Technol.

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In recent years, Simultaneous Localization And Mapping (SLAM) technology has prevailed in a wide range of applications, such as autonomous driving, intelligent robots, Augmented Reality (AR), and Virtual Reality (VR).Multi-sensor fusion using the most popular three types of sensors (e.g., visual sensor, LiDAR sensor, and IMU) is becoming ubiquitous in SLAM, in part because of the complementary sensing capabilities and the inevitable shortages (e.g., low precision and long-term drift) of the stand-alone sensor in challenging environments. In this article, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work. Firstly, a brief introduction of the state estimator formation in SLAM is presented. Secondly, the state-of-the-art algorithms of different multi-sensor fusion algorithms are given. Then we analyze the deficiencies associated with the reviewed approaches and formulate some future research considerations. This paper can be considered as a brief guide to newcomers and a comprehensive reference for experienced researchers and engineers to explore new interesting orientations.

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Multi-sensor Fusion Towards VINS: A Concise Tutorial, Survey, Framework and Challenges

Cited by 12 publications

References 27 publications

Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization

Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization

Survey on Localization Systems and Algorithms for Unmanned Systems

Camera, LiDAR, and IMU Based Multi-Sensor Fusion SLAM: A Survey

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