Efficient Multi-Band Temporal Video Filter for Reducing Human-Robot Interaction
This work addresses path planning and navigation efficiency for mobile robots, but it is incremental as it builds on existing temporal filtering and hybrid processing approaches.
The paper tackles the problem of enhancing mobile robot navigation efficiency by reducing human-robot interactions using infrastructure cameras, achieving over 8 times improvement in frames per second throughput and 6.5 times reduction in system power use with a cascade temporal filtering method.
Although mobile robots have on-board sensors to perform navigation, their efficiency in completing paths can be enhanced by planning to avoid human interaction. Infrastructure cameras can capture human activity continuously for the purpose of compiling activity analytics to choose efficient times and routes. We describe a cascade temporal filtering method to efficiently extract short- and long-term activity in two time dimensions, isochronal and chronological, for use in global path planning and local navigation respectively. The temporal filter has application either independently, or, if object recognition is also required, it can be used as a pre-filter to perform activity-gating of the more computationally expensive neural network processing. For a testbed 32-camera network, we show how this hybrid approach can achieve over 8 times improvement in frames per second throughput and 6.5 times reduction of system power use. We also show how the cost map of static objects in the ROS robot software development framework is augmented with dynamic regions determined from the temporal filter.