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VIO in dynamic environments
Visual-Inertial Odometry is a great solution for drone-navigation in GPS-denied environments. Its ability to provide centimeter-level precision in local navigation makes it a suitable choice in many commercial applications like last centimeter drone delivery. Conventional VIO algorithms work well in static environments. However, when the environment is dynamic, i.e most of the visual features come from a moving environment, for instance a moving platform, the VIO does not perform reliably. This problem can be attributed to the unreliable initialisation phase of the VIO pipeline, which is the most critical phase. Most initialisation algorithms are based on structure-from-motion, which assumes that the environment is static. In such a scenario the initialisation algorithm needs to be adapted to take into account the motion of the features.
Visual-Inertial Odometry is a great solution for drone-navigation in GPS-denied environments. Its ability to provide centimeter-level precision in local navigation makes it a suitable choice in many commercial applications like last centimeter drone delivery. Conventional VIO algorithms work well in static environments. However, when the environment is dynamic, i.e most of the visual features come from a moving environment, for instance a moving platform, the VIO does not perform reliably. This problem can be attributed to the unreliable initialisation phase of the VIO pipeline, which is the most critical phase. Most initialisation algorithms are based on structure-from-motion, which assumes that the environment is static. In such a scenario the initialisation algorithm needs to be adapted to take into account the motion of the features.
Visual-Inertial Odometry is a great solution for drone-navigation in GPS-denied environments. Its ability to provide centimeter-level precision in local navigation makes it a suitable choice in many commercial applications like last centimeter drone delivery. Conventional VIO algorithms work well in static environments. However, when the environment is dynamic, i.e most of the visual features come from a moving environment, for instance a moving platform, the VIO does not perform reliably. This problem can be attributed to the unreliable initialisation phase of the VIO pipeline, which is the most critical phase. Most initialisation algorithms are based on structure-from-motion, which assumes that the environment is static. In such a scenario the initialisation algorithm needs to be adapted to take into account the motion of the features.
The goal of this project is to develop a specialized initialization algorithm for VIO that can be used in dynamic environments.
The goal of this project is to develop a specialized initialization algorithm for VIO that can be used in dynamic environments.
- Kevin Kleber: kevinkleber@ifi.uzh.ch
- Kunal Shrivastava: shrivastava@ifi.uzh.ch
- Kevin Kleber: kevinkleber@ifi.uzh.ch - Kunal Shrivastava: shrivastava@ifi.uzh.ch