Papers
20
Total Citations
274
H-Index
9
About
Adam Schmidt is a leading researcher in visual navigation and sensor calibration for mobile robotics, with a particular focus on RGB-D perception and SLAM systems. His foundational work includes comprehensive evaluations of image feature detectors and descriptors for robot navigation, most notably his 2010 paper (53 citations) that systematically assessed the performance of various feature extraction methods in real-world robotic contexts. Schmidt has made significant contributions to benchmarking visual navigation algorithms, including the creation of an indoor RGB-D dataset (20 citations) specifically designed for evaluating navigation algorithms, and the development of calibration procedures for multi-camera motion registration systems (22 citations). His research extends to practical applications such as visual SLAM for hexapod robots (19 citations) and efficient RGB-D data processing for self-localization (13 citations). More recently, Schmidt has expanded into surgical robotics with work on multi-view surgical video action detection (12 citations) and tissue tracking using sparse neural depth and deformation techniques (9 citations). His 2020 paper on extrinsic calibration of eye-in-hand 2D LiDAR sensors (18 citations) demonstrates his continued focus on enabling robust, calibration-free sensor systems for unstructured environments.
Research Focus
Key Achievements
Top Papers
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- 5An Indoor RGB-D Dataset for the Evaluation of Robot Navigation Algorithms20 citations · 2013
- 6The Visual SLAM System for a Hexapod Robot19 citations · 2010
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- 9Multi-view Surgical Video Action Detection via Mixed Global View Attention12 citations · 2021
- 10SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Tracking9 citations · 2023