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Learning Depth from Images and IMU Measurements
In robotics, the inertial measurement unit (IMU) is a commonly used sensor and provides the motion information of the correct scale. This project aims to implement a deep learning algorithm that can estimate the scene depth from images and the IMU measurements.
Many researchers work on using deep convolutional neural network (CNN) to estimate depth from a single image. However, the depth information from a single image/camera is ambiguous in scale. Therefore, adding scale information can improve the depth estimation using CNN.
Many researchers work on using deep convolutional neural network (CNN) to estimate depth from a single image. However, the depth information from a single image/camera is ambiguous in scale. Therefore, adding scale information can improve the depth estimation using CNN.
In robotics, the inertial measurement unit (IMU) is a commonly used sensor and provides the motion information of the correct scale. This project aims to implement a deep learning algorithm that can estimate the scene depth from images and the IMU measurements.
In robotics, the inertial measurement unit (IMU) is a commonly used sensor and provides the motion information of the correct scale. This project aims to implement a deep learning algorithm that can estimate the scene depth from images and the IMU measurements.