Simultaneous Localization and Mapping algorithms for Formula Student Driverless @Technion
TODO:
- add detailed description
See demos
See wiki
Table of Contents
Installation
Roadmap
Currently working on bold
Visual Cone Detection
- annotate real data
- see instructions
- train YOLOv3-tiny
- architecture search, see yolo_cone_detection.md
- rewrite with TensorRT
- test inference performance on Xavier
- setup AirSim - next project
- add cone position information
- dataset generation pipeline
- augmentation
- weather - built in
- noise
- low light
- generate dataset
- improvements:
- add sub-pixel refinement
- add tracking
- implement KLT - works OK
- test opencv multitracker - poor
- implement ORB cone descriptor - need to test
- test Tracking-with-darkflow
Visual SLAM Exploration
- test orbslam 2
- discarded as not robust enough
- test orbslam_dwo
- discarded since no RT branch available, also slower than original orbslam
- test rovio
- works OK on servo data
- test servo on provided data
- bugfixes - doesn't compile
- compile
- test ROS node on provided data - works fine
- conclusion: continue working with servo
- need to check:
Visual SLAM System
- integrate servo with the main system
- calibrate our cameras and IMU
- test on our data
- performance optimization
- integrate with orbslam2-gpu
- add binary vocabulary
- remove Pangolin GUI
- publish points and poses to ROS
- test cone detection
- implement cone detector and descriptor
- implement data fusion in orbslam
- test on the car
LIDAR Setup
- install ROS velodyne drivers
- test and store data
- control LIDAR speed from ROS, see here
- filter LIDAR by FOV and distance, search for ring information, see here
- LIDAR-camera calibration
- lidar_camera_calibration doesn't compile, todo: debug
- try this
- velo2cam_calibration
- fusion with camera
LIDAR Cone Detection
- test the data
- add node to ROS
- ground removal
- cone segmentation and clustering
- cone tracking
- probably redundant
- additional filtering
- color from intensity
- color from camera
- improvements
LIDAR SLAM Exploration
- test off the shelf LIDAR SLAM solutions
- loam_velodyne - not good enough for tracking cones
- hdl_graph_slam - doesn't compile
- LeGO-LOAM - worse than loam_velodyne
- cartographer - compiled, looks promising, but integration is difficult
- next will unlikely work
- mrpt
- gmapping
- hector_mapping - similar to gmapping
- conclusion: general-purpose LIDAR SLAM is not good enough
LIDAR Landmark Based SLAM
System
- define ROS node topology
- add documentation
Simulator
- eufs_sim - gazebo simulator
- compiled
- bugfix - no car
- AirSim - Microsoft's framework
- fssim - a new one
Hardware
- camera - ZED
- LIDAR - Velodyne VLP-16
- Nvidia Jetson setup
- capture with rosbag
- install and run on the car
- capture demo content
- capture real content
-
Nvidia Drive PX2 setup - after meeting with Nvidia decided to use Jetson AGX Xavier
- improve capture
- find better camera + IMU, see camera considerations
- debug low fps
- automate capture - launch files
- capture new content
- Nvidia Jetson Xavier setup
- install and run on the car
- TODO: update regarding the electric car
- sensors
- control