Amirul Jamaludin
Papers
5
Total Citations
24
H-Index
3
About
Amirul Jamaludin is a robotics researcher whose work focuses on making Simultaneous Localization and Mapping (SLAM) more accessible and robust for real-world deployment. His primary research areas include SLAM algorithm development, sensor fusion, and the integration of neural networks with probabilistic filtering methods. Jamaludin’s major contribution lies in advancing Rao-Blackwellized Particle Filter (RBPF) algorithms by incorporating neural network sensor models, enabling effective SLAM performance even with low-cost, noisy range sensors. This work is particularly significant for reducing the hardware barrier in robotics, allowing for capable navigation without expensive laser rangefinders. His most cited paper (8 citations) evaluates SLAM performance on uneven terrain under varying illuminance, addressing critical challenges for outdoor and field robotics. Across his publications, Jamaludin has systematically investigated how artificial neural networks can reduce the number of particles required in RBPF, improving computational efficiency without sacrificing accuracy. His comparative studies of sampling methods and sensor models provide practical guidance for implementing cost-effective SLAM solutions. By bridging the gap between high-end sensor performance and low-cost hardware, Jamaludin’s research supports the broader democratization of autonomous navigation technology.
Research Focus
Key Achievements
Top Papers
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