Hanzhe Teng
Papers
11
Total Citations
155
H-Index
6
About
Hanzhe Teng is a robotics researcher whose work bridges autonomous navigation, environmental perception, and precision agriculture, with particular expertise in robotic exploration, simultaneous localization and mapping (SLAM), and intelligent agricultural systems. His most cited work, "Online Exploration and Coverage Planning in Unknown Obstacle-Cluttered Environments" (2020, 62 citations), tackles the fundamental challenge of enabling non-holonomic robots to achieve resolution-complete coverage without prior environmental knowledge — a capability critical for search and rescue and field monitoring applications. Teng has made significant contributions to agricultural robotics, developing adaptive LiDAR odometry and mapping systems tailored to the unique challenges of unstructured farm environments (2025, 33 citations), and releasing a multimodal dataset supporting localization and crop monitoring in citrus orchards (2023, 19 citations). His "Centroid Distance Keypoint Detector for Colored Point Clouds" (2023, 13 citations) advances computer vision by extracting both geometric and color-salient features from point clouds, improving downstream robotics perception pipelines. With contributions spanning multi-robot coordination, data-driven hierarchical control, and on-the-go tree trait estimation, Teng's cumulative body of work — exceeding 150 citations — reflects a consistent drive to deploy intelligent robotic systems in real-world, unstructured environments.
Research Focus
Key Achievements
Top Papers
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- 4Centroid Distance Keypoint Detector for Colored Point Clouds13 citations · 2023
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- 9Centroid Distance Keypoint Detector for Colored Point Clouds2 citations · 2022
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