# 0274 / Deep learning method for IMU-based tracking of Martian rover Title: Deep learning method for IMU-based tracking of Martian rover Authors: Paweł Tomiło Abstract: The double integration method is one of the most used techniques for calculating an object's position in time. As dynamic noise and bias can affect sensor data, filters like the alpha-beta filter or the Kalman filter are frequently utilized. They are in capable of compensating for sensor reading noise. The quality of the sensor has a big impact on how accurate these solutions are. When using inexpensive sensors, a correctly built neural network model can help compensate for noise and determinate the change in the position of the object over time. The article describes the method of tracking an RC vehicle using the low-cost IMU module and software consisting of an artificial neural network model specially developed for this purpose. Base stations that emitted infrared laser scans were utilized as a reference method to confirm the accuracy of the proposed solution and as a means of data collection for training. The proposed model provides acceptable accuracy when compared to the reference method. the tests were carried out on a short section of the road, in the case of longer sections the estimation error may be larger. The developed solution can be used as part of a larger system combined with visual odometry to navigate an autonomous vehicle. Order the full paper: ISTVS members: receive three papers per year as part of your membership via the ISTVS Member Portal: --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://2023.istvs.org/submissions/papers/0274.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.