AirVO: Light-Tolerant Point-Line Visual Odometry | Nanyang Technological University | Innovation and Entrepreneurship

AirVO is a lighting-robust stereo visual odometry system that combines point and line features. It uses deep learning techniques to estimate camera motion in challenging lighting conditions. The system is integrated with widely used frameworks such as ROS, OpenCV, and TensorRT. Opportunities

The global visual odometry market is expected to grow at a compound annual growth rate (CAGR) of 14.7% from 2021 to 2028, reaching USD 2.5 billion by 2028. Increasing demand for autonomous vehicles, drones, and robots is driving this growth. AirVO addresses the challenge of lighting changes, a major factor affecting the performance and reliability of visual odometry systems. By using deep learning techniques and point-line features, AirVO improves accuracy and robustness in a variety of scenarios, including indoor, outdoor, and nighttime scenes. In addition, accelerated learning-based algorithms and low-power embedded platforms reduce computational costs and power consumption, enabling deployment on lightweight devices such as smartphones, wearable devices, and ultra-small aerial vehicles. AirVO is open source and compatible with frameworks such as ROS, OpenCV, and TensorRT, making it accessible and customizable by the public community.

technology

AirVO uses deep learning techniques and point-line features to estimate camera motion in difficult lighting conditions. Its main components are:

Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) that use SuperPoint and SuperGlue algorithms to detect and match corner points in images; A line processing pipeline that associates 2D lines to 2D points and matches and triangulates lines using point and line feature distributions; A key frame selection and graph optimization module that estimates camera pose and trajectory using point and line features.

The technology enhances the performance of visual odometry in applications such as augmented reality, robotics and autonomous driving. Its accelerated learning-based algorithms and low-power embedded platforms reduce computational costs and power consumption. It is open source and based on widely used frameworks such as ROS, OpenCV and TensorRT, making it easy to implement and customize.

Applications and Benefits

Usage:

Augmented Reality Robotics Autonomous Driving

advantage:

Improved accuracy and robustness in challenging lighting conditions, including indoor, outdoor and nighttime scenes. Reduced computational cost and power consumption. Open source and based on widely used frameworks such as ROS, OpenCV and TensorRT.

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