Micro Autonomy Quest Book - Spring 2025

The Great Objective: Fully Autonomous F1tenth Racing Car with LiDAR and Camera

Micro Autonomy aims to win the F1teneth Autonomous Racing Competition in the future

2.3. Camera Integration with Localization: Integrate Camera Object Detection with Localization to use real-time slam mapping
5. Planning: Local Planning: Enable Obstacle Avoidance in lattice planner on actual vehicle
6. Control: Pure Pursuit: Integrate and tune algorithm with local planner to work on actual vehicle

1.1 Traxis Car Assembly

Score	Criteria
10/10	Fully built with all sensors and hardware mounted and power system working
8/10	Have all the sensors and Jetson mounted to the Platfrom Plate
6/10	Fabriate the Platfrom mounting plate and all other mounting hardware
4/10	Gut and stip all the components from the original chassis
2/10	Purchase & manufacture all (within current budget) components
0/10	No Progress

Progress: 10/10

1.2 LiDAR

Score	Criteria
10/10	Setup interfaces for LiDAR to ouput a ROS /scan topic
7/10	setup the docker compose file to correctly interface with the LiDAR
5/10	configure the ip and network settings for the LiDAR
0/10	No Progress

Progress: 10/10

1.3 Vec

Score	Criteria
10/10	tune the IMU to correctly how roll, pitch and yaw data and accleration values
7/10	tune the motor PID controller to produce a step response
5/10	configure all the hardware limits and current settings
2/10	Have the vesc power on and showup in the Vesc tool software
0/10	No Progress

Progress: 10/10

1.4 Overall Hardware integration

Score	Criteria
10/10	Setup interface with the correct ROS Drivers and have a tuned and accurate odometry output
7/10	configure the yaml files to fine tune odometry
5/10	Install the correct ROS Transport drivers along with F1teneth Driver Stack
0/10	No Progress

Progress: 10/10

2.1 Extended Kalman Filter

Score	Criteria
10/10	Have a fully functional Extended Kalman Filter working on the physical vehicle
8/10	Have a fully functional Extended Kalman Filter working in the simulation
6/10	Have fully Defined sensor models for the EKF
4/10	Have Fully Defined motion model for the EKF along with the corresponding Jacobian
2/10	have fully Defined state to propigate for the EKF
0/10	No Progress

Progress: 10/10

2.2 Particle Filter

Score	Criteria
5/5	Have Particle Filter working on physical vehicle
3/5	Have Particle Filter working in the simulation
0/5	No Progress

Progress: 10/10

2.3 Integrate Camera Object Detection with Localization

Score	Criteria
10/10	Localization works for changing environments in actual vehicle
5/10	Ignores LiDAR scans that interfering with real-time mapping
0/10	No Progress

Progress: 0/10

Score	Criteria
10/10	Map and yaml file generated through state estimation and LiDAR scans on actual vehicle
8/10	Map and yaml file generated through state estimation and LiDAR scanes in simulation
5/10	Map and yaml file generated through true position (odometry) and LiDAR scans in simulation
0/10	No Progress

Progress: 10/10

Score	Criteria
10/10	Optimizes centerline to generate a raceline and velocity profile that minimizes steering for all maps
7/10	Optimizes centerline to generate a raceline that minimizes steering for all maps
5/10	Generates a centerline for the vehicle to follow
0/10	No Progress

Progress: 10/10

Score	Criteria
10/10	Genenerates/optimizes local trajectory based on cost map and vehicle dynamics to achieve obstalce avoidance on actual vehicle
9/10	Genenerates/optimizes local trajectory based on cost map and vehicle dynamics to achieve obstalce avoidance
8/10	Generates local trajectory by using a cost map to achieve obstacle avoidance when obstalces are on the ideal path
5/10	Generates local trajectory to make vehicle follow the raceline (ideal path)
0/10	No Progress

Progress: 5/10

Score	Criteria
10/10	Follows local trajectories and ideal velocities smoothly on actual vehicle
7/10	Follows local trajectories and ideal velocities smoothly in simulation
4/10	Follows a global trajectory by controlling steerring angle with a constant velocity
0/10	No Progress

Progress: 7/10

Score	Criteria
10/10	Camera detects opponent cars and returns bounding boxes reliabilty in real-time with low latency
6/10	Camera detects opponent cars and returns bounding boxes
0/10	No Progress

Progress: 0/10

Quest Name	Description	Due Date	Score
Hardware Setup 1.1	Fully built with all sensors and hardware mounted and power system working	2025-08-31	10/10
Hardware Setup 1.2	Setup interfaces for LiDAR to ouput a ROS /scan topic	2025-08-31	10/10
Hardware Setup 1.3	Tune the IMU to correctly how roll, pitch and yaw data and accleration values	2025-08-31	10/10
Hardware Setup 1.4	Setup interface with the correct ROS Drivers and have a tuned and accurate odometry output	2025-08-31	10/10
State Estimation 2.1	Have a fully functional Extended Kalman Filter working on the physical vehicle	2025-08-31	10/10
State Estimation 2.2	Have Particle Filter working on physical vehicle	2025-08-31	5/5
State Estimation 2.3	Integrate Camera Object Detection with Localization to use real-time slam mapping		0/10
Mapping: SLAM	Generate a map in image format and a yaml file for the map specifications based on IMU, encoder and LiDAR specifications.	2025-08-31	10/10
Raceline Generation/Optimization	Generate a optimized raceline as global trajectory (ideal path) for the race car to follow to minimize lap time.	2025-08-31	10/10
Planning: Lattice Planner	Generate local trajectories for obstacle avoidance and following global trajectory during Racing.	2025-08-31	5/10
Controls: Pure Pursuit	Controls the vehicle's steering and trottle to following the trajectories generated by the planning module.	2025-08-31	7/10
Camera Object Detection and Edge Tracking	Camera detects opponent cars and returns bounding boxes reliabilty in real-time with low latency		0/10