Scene-Aware Error Modeling of LiDAR/Visual Odometry for Fusion-Based Vehicle Localization

Ju, Xiaoliang; Xu, Donghao; Zhao, Huijing

doi:10.1109/tits.2021.3058054

Cited by 4 publications

(1 citation statement)

References 70 publications

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“…They later extended their work to incorporate RADAR in [108]. Alternatively, in [109], a scene-aware error model is proposed for LIDAR and visual-based odometry and localisation fusion. Alternatively, researchers utilised Markov random fields with fully-connected latent variables, highlighting that the connections enable their model to consider the entire relation, and aim to identify misalignment, and localisation errors due to misalignment in [110].…”

Section: Other Tasksmentioning

confidence: 99%

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Yatbaz,

Dianati,

Woodman

2024

IEEE Trans. Intell. Transport. Syst.

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Section: Other Tasksmentioning

confidence: 99%

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Yatbaz,

Dianati,

Woodman

2024

IEEE Trans. Intell. Transport. Syst.

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Recent Advances in Mobile Robot Localization in Complex Scenarios

Zhang

Liu

et al. 2023

Lecture Notes in Electrical Engineering

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Human impressions of robot performance are often measured through surveys. As a more scalable and cost-effective alternative, we study the possibility of predicting people's impressions of robot behavior using non-verbal behavioral cues and machine learning techniques. To this end, we first contribute the SEAN TOGETHER Dataset consisting of observations of an interaction between a person and a mobile robot in a Virtual Reality simulation, together with impressions of robot performance provided by users on a 5-point scale. Second, we contribute analyses of how well humans and supervised learning techniques can predict perceived robot performance based on different combinations of observation types (e.g., facial, spatial, and map features). Our results show that facial expressions alone provide useful information about human impressions of robot performance; but in the navigation scenarios we tested, spatial features are the most critical piece of information for this inference task. Also, when evaluating results as binary classification (rather than multiclass classification), the F1-Score of human predictions and machine learning models more than doubles, showing that both are better at telling the directionality of robot performance than predicting exact performance ratings. Based on our findings, we provide guidelines for implementing these predictions models in real-world navigation scenarios.

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A Novel Bluetooth-Odometer-Aided Smartphone-Based Vehicular Navigation in Satellite-Denied Environments

Wen

Yang

Cai

et al. 2023

IEEE Trans. Ind. Electron.

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Scene-Aware Error Modeling of LiDAR/Visual Odometry for Fusion-Based Vehicle Localization

Cited by 4 publications

References 70 publications

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Introspection of DNN-Based Perception Functions in Automated Driving Systems: State-of-the-Art and Open Research Challenges

Recent Advances in Mobile Robot Localization in Complex Scenarios

A Novel Bluetooth-Odometer-Aided Smartphone-Based Vehicular Navigation in Satellite-Denied Environments

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