Joint Pose and Principal Curvature Refinement Using Quadrics
This work addresses the challenge of accurately estimating surface curvature and aligning poses in noisy point clouds, which is important for applications in computer vision and robotics, though it appears incremental as it builds on existing ICP methods.
The paper tackles the problem of surface curvature estimation and pose alignment by introducing a joint optimization approach using quadrics, achieving an order of magnitude improvement in simulation over dense relative point-to-plane ICP pose alignment and comparable pose drift to dense point-to-plane ICP bundle adjustment with low-cost depth sensors.
In this paper we present a novel joint approach for optimising surface curvature and pose alignment. We present two implementations of this joint optimisation strategy, including a fast implementation that uses two frames and an offline multi-frame approach. We demonstrate an order of magnitude improvement in simulation over state of the art dense relative point-to-plane Iterative Closest Point (ICP) pose alignment using our dense joint frame-to-frame approach and show comparable pose drift to dense point-to-plane ICP bundle adjustment using low-cost depth sensors. Additionally our improved joint quadric based approach can be used to more accurately estimate surface curvature on noisy point clouds than previous approaches.