This week, the most significant risk that could jeopardize the success of the
project is the collection of our dataset. Since the use case is limited to hallways, we found there are few sources of available datasets. One plan is to contact a former CMU PhD student about the dataset. The contingency plan is to collect it ourselves and perform data augmentations on it.
One change made to the system design is to adopt an object detection architecture similar to YOLO instead of SLAM and edge detection. This is because SLAM is more than necessary by memorizing the hallway, and edge detection is less than necessary by failing to recognize objects. The cost is to redesign the code to train the model. It will be mitigated by refactoring some parts of previous code such as data preprocessing.
The schedule is unchanged. We plan to complete the model in this week and next week.