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Navigation auf uzh.ch
Vision-based Navigation of Micro Aerial Vehicles (MAVs)
Our research goal is to develop teams of MAVs that can fly autonomously in city-like environments and that can be used to assist humans in tasks like rescue and monitoring. We focus on enabling autonomous navigation using vision and IMU as sole sensor modalities (i.e., no GPS, no laser).
Aggressive Vision-Based Flight with Quadrotors
We work on control strategies and trajectory generation algorithms to enable aggressive maneuvers with vision-based quadrotors. While pushing the agility boundaries of our quadrotors, we also enable them to recover from difficult conditions in case of a failure.
The Dynamic Vision Sensor (DVS) is a bio-inspired vision sensor that works like your eye. Instead of wastefully sending entire images at fixed frame rates, only the local pixel-level changes caused by movement in a scene are transmitted at the time they occur. The result is a stream of "address-events" at microsecond time resolution, equivalent to or better than conventional high-speed vision sensors running at thousands of frames per second.
Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. In our research, we apply deep learning to solve mobile robot navigation problems such as depth estimation, navigation, and classification.
Monocular Dense Reconstruction
We are working on the problem of estimating dense and accurate 3D maps from a single moving camera. The monocular setting is an appealing sensing modality for Micro Aerial Vehicles (MAVs), where strict limitations apply on payload and power consumption. In this case, the high agility turns the platform into a formidable depth sensor, able to deal with a wide depth range and capable of achieving arbitrarily high confidence in the measurement.
One of the goals of our research is to enable our robots to perceive their environment by changing the viewpoint of their cameras such that they obtain more or better information from it. We consider this problem of Active Vision to be of great importance to building systems that operate robustly in the real world. Within this research area, we work on perception-aware path planning to help robots navigate by choosing where to look, as well as on efficiently reconstructing objects and scenes by choosing an optimal next-best-view for the camera.
Metric 6 degree of freedom state estimation is possible with a single camera and an inertial measurement unit. Using only a monocular vision sensor, the trajectory of the camera can be recovered, up to a scale factor, using visual odometry. We investigate algorithms for visual and visual-inertial odometry, as well as methods to improve the performance of existing VO and VIO pipelines.
Teams of robots can succeed in situations where a single robot may fail. We investigate multi-robot systems composed of homogeneous or hetereogeneous robots, using both centralized and distributed communication. These teams are applied to problems in search and rescue robotics, as well as mapping and navigation.
Semantic Navigation for Robot-Human Teams
Most of the work done in localization, mapping, and navigation for both ground and aerial vehicles has been done by means of point landmarks or occupancy grids, using vision or laser range finders. However, to make these robots one day able to cooperate with humans in complex scenarios, we need to build semantic maps of the environment.
We work on intrinsic calibration of different sensors, such as omnidirectional and event-based cameras. We also work on calibration between an omnidirectional camera and 3D laser range finder.
