ME 5554 — Robotics Sensing and Navigation
4 semester hoursGraduateLectureusually offered: springtypical days: M/WBostonTraditional
Examines the actual sensors and mathematical techniques for robotic sensing and navigation with a focus on sensors such as cameras, sonars, and laser scanners. These are used in association with techniques and algorithms for dead reckoning and visual inertial odometry in conjunction with GPS and inertial measurement units. Covers Kalman filters and particle filters as applied to the SLAM problem. A large component of the class involves programming in both the ROS and LCM environments with real field robotics sensor data sets. Labs incorporate real field sensors and platforms. Culminates with both an individual design project and a team-based final project of considerable complexity.
Prerequisites
- one of:
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Spring 2024 | 1 | 13 | 15 | 87% | 2.0 |
| Spring 2025 | 1 | 7 | 8 | 88% | 1.0 |
| Spring 2026 | 1 | 8 | 10 | 80% | 2.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 54% · T 46% · W 54% · Th 25% · F 46%
Common patterns: TF (46% of sections), MW (29% of sections), MWR (25% of sections) — in patterns, R means Thursday
Professors
Spring
- Thomas Consi (54% of students) · reviews
- Mohammad Amin Javadi (46% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (ME course descriptions) · Student reviews on RateMyHusky · All ME courses · Plan it at numap.app