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This contains the code(in development) for monocular visual odometry of a quadrotor. The visual data from the monocular camera is fused with onboard IMU to develop indoor control and navigation algorithms.
Preprocessing, coordinate frame calibration, configuration files, and launching procedure used to generate point clouds with Google Cartographer for the RadMAP acquisition system. The RadMAP acquisition system consists of two LIDARS, differential GPS, two Ladybug 360 cameras, and an IMU.
Working demo: https://www.youtube.com/watch?v=v7hvOyPQ0EM. The main IoT app. It runs on Nvidia Jetson TX2(embedded super-computer). Connects with sensors via BLE and publishes all the real-time IMU data to the AWS IoT stream via MQTT. It also interacts with the user via AWS Polly text-to-speech.
The source files accompanying our research paper titled - "A Robust Approach for Improving the Accuracy of IMU based Indoor Mobile Robot Localization".