効率的で堅牢なマルチカメラ視覚慣性オドメトリに向けて
Toward Efficient and Robust Multiple Camera Visual-inertial Odometry
NVIDIA VPIを用いたGPU強化フロントエンドを導入し、CPU負荷と遅延を削減しつつマルチカメラVIOの初期化成功率とロバスト性を向上させた研究。
著者: Yao He, Huai Yu, Wen Yang, Sebastian Scherer
分類: cs.RO
原文アブストラクト
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.
