His primary scientific interests are in Artificial intelligence, Computer vision, Motion planning, Simultaneous localization and mapping and Monocular. In the subject of general Artificial intelligence, his work in Robot and Machine vision is often linked to Obstacle, thereby combining diverse domains of study. His studies deal with areas such as Artificial neural network and Convolutional neural network as well as Computer vision.
His work deals with themes such as Cognitive neuroscience of visual object recognition and Feature extraction, which intersect with Convolutional neural network. His Motion planning study integrates concerns from other disciplines, such as Human operator, Real-time computing, Ground vehicles and Collision avoidance. His research investigates the connection with Machine learning and areas like Occupancy grid mapping which intersect with concerns in Visual odometry.
His primary areas of study are Artificial intelligence, Computer vision, Motion planning, Robot and Lidar. His work on Artificial intelligence deals in particular with Robustness, Visual odometry, Point cloud, Segmentation and Robotics. His Point cloud study frequently draws parallels with other fields, such as Feature extraction.
His research integrates issues of Simultaneous localization and mapping and Odometry in his study of Computer vision. His Motion planning research is multidisciplinary, incorporating elements of Mobile robot, Real-time computing, Simulation, Mathematical optimization and Computation. The concepts of his Robot study are interwoven with issues in Cinematography and Trajectory, Control theory.
Sebastian Scherer mainly focuses on Artificial intelligence, Computer vision, Robot, Odometry and Human–computer interaction. His Feature extraction, Robotics, Robustness, Inertial measurement unit and Tracking investigations are all subjects of Artificial intelligence research. His Point cloud and Feature study, which is part of a larger body of work in Computer vision, is frequently linked to Line, bridging the gap between disciplines.
His work on Underactuation as part of general Robot research is often related to SIMPLE, Remote control and Pipeline, thus linking different fields of science. Many of his studies on Odometry apply to State as well. His Human–computer interaction study incorporates themes from Cinematography, Mobile robot and Trajectory optimization.
Sebastian Scherer mostly deals with Artificial intelligence, Computer vision, Feature extraction, Set and Pipeline. He incorporates Artificial intelligence and Block in his research. His study brings together the fields of Odometry and Computer vision.
His Feature extraction research integrates issues from Perspective and Machine learning, Feature, Feature. The study incorporates disciplines such as Optical flow, Ground truth and Benchmark in addition to Set. His Point cloud research is multidisciplinary, incorporating perspectives in Projection, Modality, Monocular camera, Lidar and Data set.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Daniel Maturana;Sebastian Scherer.
intelligent robots and systems (2015)
3D Convolutional Neural Networks for landing zone detection from LiDAR
Daniel Maturana;Sebastian Scherer.
international conference on robotics and automation (2015)
Flying Fast and Low Among Obstacles: Methodology and Experiments
Sebastian Scherer;Sanjiv Singh;Lyle Chamberlain;Mike Elgersma.
The International Journal of Robotics Research (2008)
Structure of the Type VI Secretion System Contractile Sheath
Mikhail Kudryashev;Ray Yu Ruei Wang;Maximilian Brackmann;Sebastian Scherer.
An Onboard Monocular Vision System for Autonomous Takeoff, Hovering and Landing of a Micro Aerial Vehicle
Shaowu Yang;Sebastian A. Scherer;Andreas Zell.
Journal of Intelligent and Robotic Systems (2013)
First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles
Tomoyuki Mori;Sebastian Scherer.
international conference on robotics and automation (2013)
UUV Simulator: A Gazebo-based package for underwater intervention and multi-robot simulation
Musa Morena Marcusso Manhaes;Sebastian A. Scherer;Martin Voss;Luiz Ricardo Douat.
oceans conference (2016)
River mapping from a flying robot: state estimation, river detection, and obstacle mapping
Sebastian Scherer;Joern Rehder;Supreeth Achar;Hugh Cover.
Autonomous Robots (2012)
Flying Fast and Low Among Obstacles
S. Scherer;S. Singh;L. Chamberlain;S. Saripalli.
international conference on robotics and automation (2007)
CubeSLAM: Monocular 3-D Object SLAM
Shichao Yang;Sebastian Scherer.
IEEE Transactions on Robotics (2019)
Profile was last updated on December 6th, 2021.
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