tanrye / FSTD_SLAM

Simultaneous Localization and Mapping algorithms for Formula Student Driverless at Technion

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Simultaneous Localization and Mapping algorithms for Formula Student Driverless @Technion

TODO:

  • add detailed description

Oops

See demos

See wiki

Table of Contents

Installation

See wiki/Installation

Roadmap

Currently working on bold

Visual Cone Detection

  • annotate real data
  • 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
  • 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
  • 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

LIDAR Landmark Based SLAM

  • explore directions
    • Kalman Filter vs Graph Optimization
    • g2o vs gtsam

System

  • define ROS node topology
  • add documentation

Simulator

  • eufs_sim - gazebo simulator
    • compiled
    • bugfix - no car
  • AirSim - Microsoft's framework
    • test on linux
    • add lidar, see here
    • add IMU, see here
    • integrate with ROS, see here
  • 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
  • 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

Model Predictive Control

  • explore different options:
    • MPCC Model Predictive Contouring Controller for Autonomous Racing
    • MPPI Model Predictive Path Integral Controller
  • start working on simulator

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Simultaneous Localization and Mapping algorithms for Formula Student Driverless at Technion


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