Danping Zou
Papers
12
Total Citations
438
H-Index
8
About
Danping Zou is a robotics and autonomous systems researcher whose work centers on simultaneous localization and mapping (SLAM), sensor fusion, and robot navigation. He is perhaps best known for developing M2DGR, a comprehensive multi-sensor, multi-scenario SLAM dataset for ground robots, which has garnered over 260 citations since its 2021 release and has become a widely adopted benchmark in the robotics community. His pioneering TextSLAM framework represents a significant conceptual advance in visual SLAM, uniquely integrating planar text features as both geometric and semantic landmarks — work that has attracted sustained attention across its 2020 and 2023 iterations. Zou has also contributed to robust urban localization through Sky-GVINS, a GNSS-Visual-Inertial system that leverages sky segmentation to mitigate signal degradation in dense city environments. His research portfolio extends further into visual place recognition using structural line features, LiDAR trajectory optimization, stereo-LiDAR depth estimation, and most recently, vision-based agile drone flight via differentiable physics. With a cumulative citation record exceeding 430 across his most notable works, Zou has established himself as a versatile and impactful contributor to the foundations of intelligent mobile robotics.
Research Focus
Key Achievements
Top Papers
- 1M2DGR: A Multi-Sensor and Multi-Scenario SLAM Dataset for Ground Robots261 citations · 2021
- 2TextSLAM: Visual SLAM with Planar Text Features44 citations · 2020
- 3TextSLAM: Visual SLAM With Semantic Planar Text Features38 citations · 2023
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- 5Learning vision-based agile flight via differentiable physics20 citations · 2025
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- 7Trajectory Optimization of LiDAR SLAM Based on Local Pose Graph13 citations · 2019
- 8M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots9 citations · 2021
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