Daniek Joubert

Papers

Total Citations

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About

Daniek Joubert’s research focuses on dense mapping and pose uncertainty in mobile robotics, with a particular emphasis on occupancy grid mapping. His major contribution lies in addressing the challenge of integrating range measurements into consistent world-centric maps when sensor pose is uncertain—a fundamental problem in simultaneous localization and mapping (SLAM). By applying Monte Carlo integration to occupancy grids, Joubert developed a method to robustly account for robot pose uncertainty, enabling more reliable map construction in real-world environments. His most-cited work, “Pose Uncertainty in Occupancy Grids through Monte Carlo Integration” (2014, 7 citations), and its earlier version (2013, 4 citations) demonstrate his focused impact on the SLAM community. While his citation counts are modest, these papers represent a targeted contribution to a niche but critical area of robotics—improving the accuracy of dense maps under noisy pose estimates. Joubert’s work is notable for its practical approach to a core problem, offering a foundation for further advances in autonomous navigation and mapping.