ไทยTH
UAT 474 · 3(1-4-4) · Year/term 4/1

Indoor Navigation Technology

เทคโนโลยีการนำร่องภายในอาคาร

Progress
Notional hours: 135 h (online/self-study 60 · in class/lab 75)

Course description

indoor positioning using vision, infrared, ultra-wideband, LiDAR, and inertial sensors, sensor fusion, mapping, path planning, real-time localization, and evaluation of navigation accuracy and robustness

Thai description

เทคนิคการระบุตำแหน่งภายในอาคารด้วยภาพ อินฟราเรด อัลตราไวด์แบนด์ LiDAR และเซนเซอร์เฉื่อย การรวมข้อมูลเซนเซอร์ การสร้างแผนที่ การวางแผนเส้นทาง การประมาณตำแหน่งแบบเวลาจริง และการประเมินความแม่นยำและความทนทานของระบบนำร่อง

Description source: Curriculum draft (revised 28 Sep 2026)Previous site code: DRT 474

Course learning outcomes (CLO)

CLOOutcomePLO
CLO1Explain indoor navigation technologiesPLO1PLO4
CLO2Compare positioning system accuracyPLO4
CLO3Apply SLAM in real environmentsPLO4PLO5

Learning modules

1Indoor navigation systems
Weeks 1–3 · 27 h1 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module
2UWB and markers
Weeks 4–6 · 27 h1 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module
3SLAM
Weeks 7–9 · 27 h2 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module
4Error evaluation
Weeks 10–12 · 27 h2 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module
5Sensor fusion
Weeks 13–15 · 27 h1 KU

Online (before class) · 12 h

Study the assigned knowledge units in advance, review media and take the module quiz

In class / field · 15 h

Lab or field practice from worksheets with a safety checklist

Learning evidence: Checked worksheets and quiz results

Open module

Assessment (draft)

Labs and worksheets35%
Module quizzes10%
Midterm examination20%
Mini-project or practical exam35%

Knowledge domain

D04 Sensors and embedded systemsD06 Control, autopilot and navigationD12 Automation, robotics and swarmsD15 Delivery, indoor operations and warehousing

Key references

  1. Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., & Leonard, J. J. (2016). Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on Robotics, 32(6), 1309–1332. link
  2. Campos, C., Elvira, R., Gómez Rodríguez, J. J., Montiel, J. M. M., & Tardós, J. D. (2021). ORB-SLAM3: An accurate open-source library for visual, visual–inertial, and multimap SLAM. IEEE Transactions on Robotics, 37(6), 1874–1890. link
  3. Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. MIT Press. link