Monocular visual odometry-based 3D-2D motion estimation

2018 ◽  
Author(s):  
Tongwei Lu ◽  
Shihui Ai ◽  
Yudian xiong ◽  
Yongyuan Jiang
Keyword(s):  
Motion Estimation ◽  
Visual Odometry

Visual Odometry Based on 3D-3D and 3D-2D Motion Estimation Method

2018 ◽  
Author(s):  
Kai Cao ◽  
Xuemeng Yang ◽  
Song Gao ◽  
Chaobo Chen ◽  
Jiaoru Huang ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Estimation Method ◽  
Visual Odometry

Motion Estimation Using Visual Odometry and Deep Learning Localization

2017 ◽  
Vol 2017 (19) ◽  
pp. 62-69 ◽  
Author(s):  
Suvam Bag ◽  
Vishwas Venkatachalapathy ◽  
RaymondW. Ptucha
Keyword(s):  
Deep Learning ◽  
Motion Estimation ◽  
Visual Odometry

scholarly journals Lightweight Visual Odometry for Autonomous Mobile Robots

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2837 ◽  
Author(s):  
Mohamed Aladem ◽  
Samir Rawashdeh
Keyword(s):  
Motion Estimation ◽  
Effective Means ◽  
Visual Odometry ◽  
Frame Rate ◽  
Autonomous Mobile Robots ◽  
Modular Software ◽  
Mobile Robot Localization ◽  
And Performance ◽  
Local Map ◽  
Open Source Systems

Vision-based motion estimation is an effective means for mobile robot localization and is often used in conjunction with other sensors for navigation and path planning. This paper presents a low-overhead real-time ego-motion estimation (visual odometry) system based on either a stereo or RGB-D sensor. The algorithm’s accuracy outperforms typical frame-to-frame approaches by maintaining a limited local map, while requiring significantly less memory and computational power in contrast to using global maps common in full visual SLAM methods. The algorithm is evaluated on common publicly available datasets that span different use-cases and performance is compared to other comparable open-source systems in terms of accuracy, frame rate and memory requirements. This paper accompanies the release of the source code as a modular software package for the robotics community compatible with the Robot Operating System (ROS).

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