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Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving

June 2020

tl;dr: object SLAM that uses 3d mod proposals from each frame.

Overall impression

The demo is quite impressive. Almost as good as cube slam. The 3D object proposal step is quite simple yet effective for cars like sedans with strong shape priors, even simpler than the deep3dbox method.

Many insightful comments from the paper:

End to end 3D regression need lots of training data and require heavy workload to precisely label all the object bboxes in 3D. Instance 3D detection produces frame-independent results, which are not consistent enough for continuous perception in autonomous driving.

Purely depending on instance 2D bbox limits its performance in predicting pose for truncated object.

The paper proposed a novel object bundle adjustment (BA). The method can track 3D objects and recover he dynamic sparse point cloud with instance accuracy and temporal consistency.

The method can track the object continuously even for the extremely truncated case where object pose is hard for instance inference.

Key ideas

  • 3d bbox proposal:
    • 2D bbox + 8 viewpoint classification + shape dimension prior
    • Assumption: reprojection of 3D bbox will tightly fit the 3d bbox. --> This assumption is not exactly true. But for most autonomous driving cases (horizontal or slightly looking down) the assumption holds true quite well.
  • Ego motion tracking
    • Filter out dynamic objects and do slam to get pose. Similar to orb-slam.
  • Object tracking: 4 residual loss terms
    • feature reprojection (feature points on objects has fixed coordinates in object frame)
    • 2D bbox consistency (projection of 3D tracked object should fit into 2D measurement)
    • 3D pose consistency (temporally predicted pose vs inferred measurement from frame). A kinematics motion model to ensure consistent orientation and motion estimation. It involves vehicle dim, speed, steer angle.
    • object dimension consistency
  • Dynamic Point cloud alignment (for stereo only)
    • After getting pose, align it with feature point cloud with accurate depth information from stereo.

Technical details

  • Dimension of objects are invariant!
  • Adding feature point tracking helps with easy cases (large, untruncated cars), while adding dynamic point cloud alignment helps with hard cases (faraway, truncated cars)

Notes