{"id":121,"date":"2020-01-15T04:47:49","date_gmt":"2020-01-15T04:47:49","guid":{"rendered":"http:\/\/mrsdprojects.ri.cmu.edu\/2020teami\/?page_id=121"},"modified":"2020-05-08T13:47:10","modified_gmt":"2020-05-08T13:47:10","slug":"system-requirements","status":"publish","type":"page","link":"https:\/\/mrsdprojects.ri.cmu.edu\/2020teami\/system-requirements\/","title":{"rendered":"System Requirements"},"content":{"rendered":"

Mandatory Performance Requirements<\/h2>\n\n\n\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t
M.P.1<\/td>The system will explore the garage and reach a target parking spot within 2t time, where t is the time it takes to reach the target spot via user teleoperation<\/td>\n<\/tr>\n
M.P.2<\/td>The system will create a map of the environment with a mapping error of less than 20 cm as compared to the ground truth map<\/td>\n<\/tr>\n
M.P.3<\/td>The system\u2019s map-building will be robust over sharp turns, such that the SLAM pipeline will not break (lose track of correspondences) in 80% of trials where a 90 degree turn is present<\/td>\n<\/tr>\n
M.P.4<\/td>The system will discretely localize itself within 5 degrees in rotation and 50 cm in translation as compared to the ground truth location<\/td>\n<\/tr>\n
M.P.5<\/td>The system will detect lanes of correct length and position in the image in 80% of the frames<\/td>\n<\/tr>\n
M.P.6<\/td>The system will create exploration waypoints that lie inside the lane in 80% of trials<\/td>\n<\/tr>\n
M.P.7<\/td>The system will park itself in the target parking spot within 10 degrees and 75 cm of the target pose<\/td>\n<\/tr>\n
M.P.8<\/td>The system will build a semi-dense map of its environment in near-real-time (with a delay of less than 1 second between receiving sensor data and updating the map)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

Mandatory Nonfunctional Requirements<\/h2>\n\n\n\n\n\t\n\t
M.N.1<\/td>System\u2019s exploration algorithm will be scalable to a full-size autonomous car<\/td>\n<\/tr>\n
M.N.2<\/td>Prototype will be inexpensive i.e., less than $5000 to fabricate and assemble<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

Desirable Performance Requirements<\/h2>\n\n\n\n\n\t\n\t\n\t
D.P.1<\/td>System will avoid dynamic obstacles, stopping 100% of the time a dynamic obstacle is introduced in the lane within 1m from the front of the robot<\/td>\n<\/tr>\n
D.P.2<\/td>System will detect empty parking spots using raw sensor data (no AprilTags) with an accuracy of 80%<\/td>\n<\/tr>\n
D.P.3<\/td>System will detect and classify road signs with a Mean Average Precision score of 80<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

Desirable Nonfunctional Requirements<\/h2>\n\n\n\n\n\t\n\t\n\t
D.N.1<\/td>The system\u2019s architecture and software framework will be well-documented<\/td>\n<\/tr>\n
D.N.2<\/td>The system will notify the user of the status and location of the car once it is parked<\/td>\n<\/tr>\n
D.N.3<\/td>The system will choose between several open parking spots based on a heuristic<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n

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