Adam Niewola
Papers
8
Total Citations
67
H-Index
4
About
Adam Niewola is a robotics researcher whose work focuses on enabling autonomous mobile robot navigation in some of the most challenging environments—GPS-denied, dark, and rough terrains such as caves, mines, and outdoor non-urban areas. His key research areas include path planning, 6D localization, and LiDAR-based mapping. Niewola’s major contribution is the development of the L* algorithm (2017, 28 citations), a novel graph-searching method for global path planning that achieves linear computational complexity, outperforming the traditional A* algorithm by eliminating the need for heap-sorted open lists. He also pioneered the PSD (Point-to-Surfel-Distance) probabilistic algorithm for 6D robot localization without natural or artificial landmarks, using 2.5D maps and laser scanners (2019, 12 citations). His work on real-time parallel-serial LiDAR-based localization (2020, 7 citations) achieves centimeter accuracy in GPS-denied scenarios, while his modification of Gaussian mixture maps (2020, 8 citations) enables robust pose estimation in rough terrain. Niewola’s algorithms are notable for their computational efficiency and practical applicability in extreme environments, making him a key contributor to the field of field robotics and autonomous exploration.
Research Focus
Key Achievements
Top Papers
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- 5Selected aspects of robin heart robot control4 citations · 2013
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- 7Rough surface description system in 2,5D map for mobile robot navigation3 citations · 2013
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