Papers
4
Total Citations
172
H-Index
4
About
Jason Luck is a researcher whose work sits at the intersection of 3D data processing and human-robot interaction, with a particular focus on the challenges of real-world robotic deployment. His most significant contribution is the development of a hybrid algorithm for registering range data, which combines simulated annealing with the iterative closest point (ICP) method. This approach, detailed in his 2002 paper (87 citations), directly addressed a core difficulty in applications like object recognition, robotic navigation, and reverse engineering: aligning overlapping 3D images accurately and robustly. Luck’s work on this problem provided a more reliable solution for building coherent world models from noisy sensor data. Beyond geometric modeling, he has also investigated the critical issue of communication latency in controlling robotic assets, particularly for military applications. His 2006 study (76 citations) experimentally quantified how delays in network links degrade operator performance and robot control, offering foundational insights for designing more resilient teleoperation systems. Together, these contributions demonstrate a career focused on bridging the gap between theoretical algorithms and the practical, often imperfect, conditions of real-world robotics.
Research Focus
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