Significant risk: Localization
During the design presentation, we received feedback about potential issues with localization when strictly using the odometry sensor. We believe that part of this was due to confusion about how our sensor works, as the main issue brought up was how optical sensors can be inaccurate. We tried to explain that the odometry sensor uses both an optical sensor and an IMU and cross-references readings from both to get more accurate data than either one individually.
In spite of what we see as a misunderstanding of how the sensor works, it does raise a good point about redundancy for localization. Our odometry sensor is rated to be less than 1% inaccurate when calibrated for the surface it is going to be used on. However, the surfaces of CMU buildings vary, and because we have not tested the variance between them, we do not know if the sensor would maintain a similar level of accuracy when traversing different parts of a single building. Even if it maintain its sub-1% inaccuracy on all surfaces, any inaccuracy at all leads to drift as the robot continues to travel. For resets of paths, we had planned on creating an identifiable “reset point” that the robot knows to reset its localization drift.
To address the concern about drift, we can expand on this idea of reset points through the creation of “landmarks” along the paths that the robot can potentially take, resulting in multiple points of recalibration and error reset during any given path. Three forms of markers that we have explored are QR codes, ArUco markets and AprilTags, all of which are systems of black and white squares that encode information based on the pattern, allowing the robot to distinguish between them. In addition, their standardization means that the angles of their edges relative to the camera and each other can be used to identify distance.
Schedule change: Due to an unforseen situation preventing Paul from properly working this week, he is doing last week’s work this week, and this week’s work during spring break.