1. Introduction
2. Kalman Filter
3. Extended Kalman Filter
4. Map Based Localization
5. Feature Based EKF SLAM
6. Pose Based EKF SLAM
30% Laboratory Exercices
70% Theory & Exercices. Evaluated through continuous evaluations examination, plus a final evaluation.
About the Use of AI
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AI may be used to:
- Clarify theoretical concepts related to the laboratory.
- Explain scientific or engineering principles.
- Improve the grammar, spelling, and readability of laboratory reports.
- Help understand feedback provided by the instructors.
AI must not be used to:
- Write or generate code, scripts, or programs for the laboratory assignments.
- Solve the laboratory exercises on your behalf.
- Generate data, results, analyses, or conclusions that are presented as your own work.
All code submitted in this course must be written by the student unless explicitly stated otherwise by the instructor. Students are expected to understand, design, implement, and debug their own solutions.
If AI is used for any permitted purpose (e.g., language editing or conceptual clarification), its use should be acknowledged briefly in the submitted report.
Students remain fully responsible for the accuracy, originality, and integrity of all submitted work. Misrepresenting AI-generated content as one's own work constitutes academic misconduct and will be handled according to the University's academic integrity regulations.
During Evaluation Activities
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Students must enter the classroom where the assessment activity is to take place with all communication devices — mobile phones, computers, tablets, smartwatches, etc. — switched off and kept inside their backpacks/bags. Failure to comply with this rule will result in a grade of 0 for the activity, as well as the implementation of the actions described in Article 21 of the UdG regulations governing student assessment and grading processes.
If, during the correction process of the assessment activity, the lecturer determines the existence of possible fraud, they reserve the right to validate the grade obtained using the assessment methodology they deem appropriate.
Criteris específics de la nota «No Presentat»:
When anyone of the parts is not submitted.
Avaluació única:
The same evaluation activities will be carried out but facilitating that those activities that require a compulsory presence in the laboratory could be done either in person at agreed times, or remotely using robot simulators. Deadlines will also be adjusted so that a single delivery of all activities can be made.
Requisits mínims per aprovar:
Every part (Lab & Examination) must have a mark beyond 5 to pass the course.
Knowledge of phyton and MATLAB is assumed. This programming language will not be taught. Although it is possible to complete the lab work with the laboratory computers, it is recommended to bring your own laptop to the lab to make it easier to complete the work at home. A virtual machine with ubuntu, ROS and the Turtlebot SDK will be provided.
There will be a Turtlebot available for every 2 students.