HD-MAC: A Throughput Enhancing TDMA-Based Protocol in High Dynamic Environments

2016 ◽  
Author(s):  
Tianyi Wang ◽  
Xiong Wang ◽  
Xiaohua Tian ◽  
Xiaoying Gan
Keyword(s):  
Dynamic Environments ◽  
High Dynamic

Adaptive Kalman filtering methods for tracking GPS signals in high noise/high dynamic environments

2007 ◽  
Author(s):  
Qiyao Zuo ◽  
Hong Yuan ◽  
Baojun Lin
Keyword(s):  
Kalman Filtering ◽  
Dynamic Environments ◽  
High Noise ◽  
High Dynamic

scholarly journals UAV navigation in high dynamic environments: A deep reinforcement learning approach

2020 ◽  
Author(s):  
Tong GUO ◽  
Nan JIANG ◽  
Biyue LI ◽  
Xi ZHU ◽  
Ya WANG ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Environments ◽  
Learning Approach ◽  
High Dynamic ◽  
Uav Navigation

Discrete chirp‐Fourier transform‐based acquisition algorithm for weak global positioning system L5 signals in high dynamic environments

2013 ◽  
Vol 7 (7) ◽  
pp. 736-746 ◽  
Author(s):  
Bo Fan ◽  
Kai Zhang ◽  
Yuliang Qin ◽  
Hongqiang Wang ◽  
Huaitie Xiao
Keyword(s):  
Fourier Transform ◽  
Global Positioning System ◽  
Dynamic Environments ◽  
Positioning System ◽  
Global Positioning ◽  
High Dynamic

High Order Uncompressed Coning Algorithm in High Dynamic Environments

2019 ◽  
Author(s):  
Hui Zhao ◽  
Zhong Su ◽  
Fuchao Liu ◽  
Chao Li ◽  
Qing Li
Keyword(s):  
Dynamic Environments ◽  
High Order ◽  
High Dynamic

DUDMap: 3D RGB-D mapping for dense, unstructured, and dynamic environment

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110161
Author(s):  
Özgür Hastürk ◽  
Aydan M Erkmen
Keyword(s):  
Real Time ◽  
Dynamic Environment ◽  
Semantic Segmentation ◽  
Small Region ◽  
Dynamic Environments ◽  
Reconstruction Error ◽  
Localization And Mapping ◽  
Segmentation Methods ◽  
High Dynamic ◽  
Better Than

Simultaneous localization and mapping (SLAM) problem has been extensively studied by researchers in the field of robotics, however, conventional approaches in mapping assume a static environment. The static assumption is valid only in a small region, and it limits the application of visual SLAM in dynamic environments. The recently proposed state-of-the-art SLAM solutions for dynamic environments use different semantic segmentation methods such as mask R-CNN and SegNet; however, these frameworks are based on a sparse mapping framework (ORBSLAM). In addition, segmentation process increases the computational power, which makes these SLAM algorithms unsuitable for real-time mapping. Therefore, there is no effective dense RGB-D SLAM method for real-world unstructured and dynamic environments. In this study, we propose a novel real-time dense SLAM method for dynamic environments, where 3D reconstruction error is manipulated for identification of static and dynamic classes having generalized Gaussian distribution. Our proposed approach requires neither explicit object tracking nor object classifier, which makes it robust to any type of moving object and suitable for real-time mapping. Our method eliminates the repeated views and uses consistent data that enhance the performance of volumetric fusion. For completeness, we compare our proposed method using different types of high dynamic dataset, which are publicly available, to demonstrate the versatility and robustness of our approach. Experiments show that its tracking performance is better than other dense and dynamic SLAM approaches.

Sign in / Sign up

Export Citation Format

Share Document