Alexander Schiotka
Papers
1
Total Citations
9
H-Index
1
About
Alexander Schiotka is a roboticist whose work centers on efficient environmental perception and localization for autonomous systems. His key research areas include robot navigation, sensor-based mapping, and memory-efficient spatial representations. Schiotka’s most notable contribution is his 2017 paper, "Robot localization with sparse scan-based maps," which tackles the critical challenge of high memory demand in occupancy grid maps—a standard method for representing environments in robot navigation. By introducing a sparse, scan-based map representation, he demonstrated how to maintain localization accuracy while dramatically reducing memory consumption, which grows quadratically with sensor range in traditional approaches. This work, with 9 citations, has influenced subsequent research on lightweight mapping for resource-constrained robots. Schiotka’s contributions are particularly valuable for applications requiring long-range sensing on platforms with limited computational resources, such as drones or small ground vehicles. His research bridges the gap between theoretical mapping algorithms and practical deployment constraints, making him a notable figure in the field of efficient robotic perception.
Research Focus
Key Achievements
Top Papers
- 1Robot localization with sparse scan-based maps9 citations · 2017