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About

Nattee Niparnan is a leading researcher in autonomous mobile robotics, with a focus on safe navigation, simultaneous localization and mapping (SLAM), and human-robot interaction. His most impactful work, "Robust local obstacle avoidance for mobile robot based on Dynamic Window approach" (53 citations), advances the Dynamic Window approach for real-time, collision-free path planning in cluttered environments. He has also made significant contributions to vision-based SLAM, notably developing a bearing-only FastSLAM system using vertical lines from an omnidirectional camera (14 citations), which enables robust indoor localization without range data. In the area of 3D perception, Niparnan proposed an efficient plane detection method for Kinect image sequences (10 citations), a critical capability for navigation in man-made environments. His work extends to integrated robotic systems, including a SLAM-based grasping framework that unifies object modeling, grasp planning, and arm navigation (6 citations), as well as a human-tracking method using foot positions from depth cameras for side-by-side walking robots (5 citations). Through these contributions, Niparnan has advanced practical, real-world mobile robot autonomy, bridging perception, planning, and interaction.