Search Results for author: Ling Pei

Found 11 papers, 4 papers with code

TextSLAM: Visual SLAM with Semantic Planar Text Features

1 code implementation17 May 2023 Boying Li, Danping Zou, Yuan Huang, Xinghan Niu, Ling Pei, Wenxian Yu

The results show that integrating texture features leads to a more superior SLAM system that can match images across day and night.

Mixed Reality Scene Understanding

NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping

1 code implementation19 Mar 2023 Junyuan Deng, Xieyuanli Chen, Songpengcheng Xia, Zhen Sun, Guoqing Liu, Wenxian Yu, Ling Pei

To bridge this gap, in this paper, we propose a novel NeRF-based LiDAR odometry and mapping approach, NeRF-LOAM, consisting of three modules neural odometry, neural mapping, and mesh reconstruction.

RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous Driving

1 code implementation9 Mar 2023 Xiuyu Yang, Zhuangyan Zhang, Haikuo Du, Sui Yang, Fengping Sun, Yanbo Liu, Ling Pei, Wenchao Xu, Weiqi Sun, Zhengyu Li

Then we implement muti-type sensor detection and multi-group sensors fusion in this environment, including camera-radar and camera-lidar detection based on result-level fusion.

Autonomous Driving Scheduling

Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition

no code implementations16 Aug 2022 Songpengcheng Xia, Lei Chu, Ling Pei, Wenxian Yu, Robert C. Qiu

Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications.

Activity Prediction Human Activity Recognition

MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model

no code implementations20 Sep 2020 Ling Pei, Songpengcheng Xia, Lei Chu, Fanyi Xiao, Qi Wu, Wenxian Yu, Robert Qiu

Together with the rapid development of the Internet of Things (IoT), human activity recognition (HAR) using wearable Inertial Measurement Units (IMUs) becomes a promising technology for many research areas.

Human Activity Recognition Transfer Learning

Attention-SLAM: A Visual Monocular SLAM Learning from Human Gaze

1 code implementation15 Sep 2020 Jinquan Li, Ling Pei, Danping Zou, Songpengcheng Xia, Qi Wu, Tao Li, Zhen Sun, Wenxian Yu

This paper proposes a novel simultaneous localization and mapping (SLAM) approach, namely Attention-SLAM, which simulates human navigation mode by combining a visual saliency model (SalNavNet) with traditional monocular visual SLAM.

Simultaneous Localization and Mapping

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

no code implementations7 Apr 2020 You Li, Yuan Zhuang, Xin Hu, Zhouzheng Gao, Jia Hu, Long Chen, Zhe He, Ling Pei, Kejie Chen, Maosong Wang, Xiaoji Niu, Ruizhi Chen, John Thompson, Fadhel Ghannouchi, Naser El-Sheimy

Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges.

Networking and Internet Architecture Signal Processing

A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors

no code implementations4 Mar 2020 Fanyi Xiao, Ling Pei, Lei Chu, Danping Zou, Wenxian Yu, Yifan Zhu, Tao Li

The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91. 15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method.

Human Activity Recognition Transfer Learning

TextSLAM: Visual SLAM with Planar Text Features

no code implementations26 Nov 2019 Boying Li, Danping Zou, Daniele Sartori, Ling Pei, Wenxian Yu

We propose to integrate text objects in man-made scenes tightly into the visual SLAM pipeline.

Scene Understanding

LEMO: Learn to Equalize for MIMO-OFDM Systems with Low-Resolution ADCs

no code implementations14 May 2019 Lei Chu, Ling Pei, Husheng Li, Robert Caiming Qiu

This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS).

StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments

no code implementations16 Oct 2018 Danping Zou, Yuanxin Wu, Ling Pei, Haibin Ling, Wenxian Yu

Instead of using Manhattan world assumption, we use Atlanta world model to describe such regularity.


Cannot find the paper you are looking for? You can Submit a new open access paper.