Oduetse Matsebe
Papers
2
Total Citations
6
H-Index
2
About
Oduetse Matsebe is a robotics researcher whose work centers on autonomous navigation, with a particular focus on the simultaneous localization and mapping (SLAM) problem for underwater vehicles. His research addresses one of robotics' most fundamental challenges: enabling an autonomous vehicle to build a map of an unknown environment while simultaneously keeping track of its own location within that map. Matsebe's key contributions include developing kinematic models for autonomous underwater vehicles (AUVs) specifically tailored for range-bearing SLAM applications, and providing clear, accessible explanations of the Extended Kalman Filter (EKF) approach to SLAM. His 2008 paper on modeling AUV kinematics for SLAM has accumulated 3 citations, while his 2010 work on basic EKF-SLAM has also garnered 3 citations. Though modest in citation count, these works serve as important educational resources, helping students and researchers understand the transition from simple EKF-based methods to more complex probabilistic approaches. Matsebe's particular contribution lies in bridging theoretical SLAM formulations with practical implementation for underwater robotics, a domain where sensor limitations and environmental uncertainty pose unique challenges.
Research Focus
Key Achievements
Top Papers
- 1
- 2Basic Extended Kalman Filter – Simultaneous Localisation and Mapping3 citations · 2010