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Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning

December 2020

tl;dr: An overview of deep learning application in SLAM for autonomous driving.

Overall impression

The paper provides a good overview of VSLAM applications in autonomous driving.

Key ideas

  • Three main scenarios for autonomous driving: highway, parking lot and city
    • Parking: needs an accurate environment map in the near vicinity of the car while driving at low speed.
    • Highway: 20 FPS or more.
      • Orb-SLAM is doing a great job already in highway scenes.
    • City: Many dynamic objects that needs to be detected actively or passively during 3D reconstruction.
      • Orb-SLAM performs poorly. The ability to reconstruct static points with stability against lots of dynamic objects within the scene is key.
  • Two approaches to autonomous driving
    • Mediated perception approach
    • End-to-end approach. Perception can be used as auxiliary supervision.
  • Two main types of HD map
    • Dense semantic point cloud maps
      • TomTom, Google
      • Mapped with lidar/camera
      • Provides strong prior to semantic segmentation
    • Semantic landmarks maps
      • MobileEye, HERE
      • Mapped with camera
  • Private small scale map
    • Small scale mapping capability is necessary
    • Privacy, Coverage and dynamic
  • Fundamental pipelines of SLAM
    • Tracking (Visual Odometry, frontend)
    • Mapping: sparse (feature based method) or dense (direct method)
    • Global optimization and loop closure
    • Re-localization
  • History of SLAM
    • Feature based SLAM
      • MonoSLAM: EKF-tracking
      • PTAM: bundle adjustment
      • Orb-SLAM: loop closure + global pose optimization
    • Direct SLAM
      • DTAM: photometric error
      • LSD-SLAM: loop closure + global pose optimization
      • DSO: geometric error, lens distortion, exposure time calib
  • Deep learning opportunities in SLAM
    • depth estimation
    • optical flow
    • feature correspondence
    • bundle adjustment
    • semantic segmentation
    • camera pose estimation

Technical details

  • Stereo SLAM are acceptable for autonomous driving applications, but monocular results are weak and unacceptable.
  • Rolling shutter has to be accounted for on highways for accurate SLAM
  • There is still no mature solutions for how to do self-repairing map, and map on the vehicle.
  • We need motion segmentation for general obstacle detection.

Notes

  • Do we really need 20 FPS for parking lot and city?