Toward Efficient and Robust Multiple Camera Visual-inertial Odometry
This work addresses efficiency and robustness challenges in VIO for applications like robotics or autonomous systems, but it is incremental as it builds on existing GPU and multi-camera insights.
The paper tackled the problem of high CPU resource usage and computational latency in visual-inertial odometry (VIO) systems by proposing a GPU-enhanced front-end using NVIDIA VPI, which reduced CPU occupation by 40.4% and latency by 50.6% without accuracy loss, and improved robustness with a multi-camera setup.
Efficiency and robustness are the essential criteria for the visual-inertial odometry (VIO) system. To process massive visual data, the high cost on CPU resources and computation latency limits VIO's possibility in integration with other applications. Recently, the powerful embedded GPUs have great potentials to improve the front-end image processing capability. Meanwhile, multi-camera systems can increase the visual constraints for back-end optimization. Inspired by these insights, we incorporate the GPU-enhanced algorithms in the field of VIO and thus propose a new front-end with NVIDIA Vision Programming Interface (VPI). This new front-end then enables multi-camera VIO feature association and provides more stable back-end pose optimization. Experiments with our new front-end on monocular datasets show the CPU resource occupation rate and computational latency are reduced by 40.4% and 50.6% without losing accuracy compared with the original VIO. The multi-camera system shows a higher VIO initialization success rate and better robustness overall state estimation.