A Flexible Framework for Virtual Omnidirectional Vision to Improve Operator Situation Awareness
This work addresses the need for better operator awareness in teleoperation to prevent mission failures, though it appears incremental as it builds on existing sensor fusion techniques.
The paper tackles the problem of limited field-of-view in camera streams for teleoperated mobile robots by introducing a flexible framework that fuses multiple cameras and integrates Lidar data to create virtual omnidirectional projections, resulting in improved situation awareness and reduced system complexity compared to traditional methods.
During teleoperation of a mobile robot, providing good operator situation awareness is a major concern as a single mistake can lead to mission failure. Camera streams are widely used for teleoperation but offer limited field-of-view. In this paper, we present a flexible framework for virtual projections to increase situation awareness based on a novel method to fuse multiple cameras mounted anywhere on the robot. Moreover, we propose a complementary approach to improve scene understanding by fusing camera images and geometric 3D Lidar data to obtain a colorized point cloud. The implementation on a compact omnidirectional camera reduces system complexity considerably and solves multiple use-cases on a much smaller footprint compared to traditional approaches such as actuated pan-tilt units. Finally, we demonstrate the generality of the approach by application to the multi-camera system of the Boston Dynamics Spot. The software implementation is available as open-source ROS packages on the project page https://tu-darmstadt-ros-pkg.github.io/omnidirectional_vision.