What did you personally accomplish this week on the project?
This week I focused on investigating alternative localization approaches after our professor had skepticism about relying solely on the optical flow sensor (PAA5160) for accurate navigation. We received feedback that odometry localization may not be reliable enough for our accuracy requirements. The main alternative I explored was marker systems, specifically ArUco markers, AprilTags, and QR codes that could be placed at known locations throughout the building hallway. The idea is that our existing camera could detect these markers and use them to reset or correct position estimates during navigation, compensating for any drift accumulated by the optical flow sensor.
I evaluated several sensor fusion architectures that could combine multiple localization inputs. The most promising approach uses the odometry sensor for continuous tracking between landmarks and visual fiducial markers for periodic position correction. This hybrid system would leverage the strengths of each component while mitigating their individual weaknesses. For implementation I researched how the OpenCV ArUco detection library could integrate with our existing camera hardware and CV pipeline. A hybrid approach using optical odometry for continuous tracking with periodic visual landmark corrections could achieve our position error requirements while being significantly more robust than odometry alone. ArUco markers can be detected in real time with our YOLOv3-tiny human detection without creating a significant performance bottleneck.
Is your progress on schedule or behind?
According to our Gantt chart, Task 3.1 should be complete by the end of Week 2. While I’ve made progress researching the optical sensor and understanding its capabilities, the professor’s feedback revealed that we need a different solution. This puts me a few days behind on finalizing our localization approach since we now need to integrate additional components we hadn’t initially budgeted time for. Our team will make a decision on our approach very soon, whether that’s pure odometry or odometry with visual landmarks.
What deliverables do you hope to complete in the next week?
My primary deliverable is getting the optical flow sensor operational with working code. I need to write the interface code to communicate with the PAA5160 sensor and pull X/Y displacement readings. This will be implemented in Python interfacing through the Arduino. Once I have raw sensor data coming through, I’ll implement a basic position tracking algorithm that integrates the displacement values over time to estimate the robot’s current position.