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Localization Of People In Gnss-Denied Environments Using Neural-Inertial Prediction And Kalman Filter Correction, Lauren N. Cash
Localization Of People In Gnss-Denied Environments Using Neural-Inertial Prediction And Kalman Filter Correction, Lauren N. Cash
Graduate Theses, Dissertations, and Problem Reports
This thesis presents a method based on neural networks and Kalman filters for estimating the position of a person carrying a mobile device (i.e., cell phone or tablet) that can communicate with static UWB sensors or is carried in an environment with known landmark positions. This device is used to collect and share inertial measurement unit (IMU) information — which includes data from sensors such as accelerometers, gyroscopes, and magnetometers — and UWB and landmark information. The collected data, in combination with other necessary initial condition information, is input into a pre-trained deep neural network (DNN) which predicts the movement …
Ego-Localization Navigation For Intelligent Vehicles Using 360° Lidar Sensor For Point Cloud Mapping, Tyler Naes
Ego-Localization Navigation For Intelligent Vehicles Using 360° Lidar Sensor For Point Cloud Mapping, Tyler Naes
Electronic Theses and Dissertations
With its prospects of reducing vehicular accidents and traffic in highly populated urban areas by taking the human error out of driving, the future in automobiles is leaning towards autonomous navigation using intelligent vehicles. Autonomous navigation via Light Detection And Ranging (LIDAR) provides very accurate localization within a predefined, a priori, point cloud environment that is not possible with Global Positioning System (GPS) and video camera technology. Vehicles may be able to follow paths in the point cloud environment if the baseline paths it must follow are known in that environment by referencing objects detected in the point cloud …