Modified ICP Algorithm for Tracking of CAD Geometries using Depth Data

As both cameras and processors have improved massively over the years, the application range for object tracking algorithms has grown as well, now reaching from augmented reality over visual perception in robotics to very precise real-time visual quality control in the industry. However, the current approaches around RGB tracking lack robustness for bad lighting conditions or models without strong and distinct edges which creates a need for algorithms on depth data. Most current depth tracking algorithms are based on the Iterative Closest Point algorithm to align two depth measurements. So far, ICP variants have strictly kept the creation of correspondences and the minimization of the global error over these correspondences separate. The research goal of this work is to design and implement a modified ICP frame-to-model tracking pipeline with integrated Projective Data Association in the optimization loop for additional efficiency. After that, we enhance the normal vector computation, interpolation techniques for the Projective Data Association, and outlier removal strategies. By testing on synthetic data, we can show that these modular refinements improve the overall tracking performance in terms of precision, robustness, convergence radius, and efficiency. The total pipeline reaches a convergence rate of around 30% on the synthetic data in our test setup. While this does not suffice for applications in the real world yet, it still shows that the approach looks promising and bears potential for future research on further enhancements.