通过联合优化感知、通信与移动速度，降低长期导航的能耗。
Energy-Efficient SLAM via Joint Design of Sensing, Communication, and Exploration Speed
- 联合优化感知时长、传输功率、传输时长和移动速度
- 实测与仿真表明能耗显著降低，支持长期运行
- 适合需要持久运行的机器人导航系统
为支持未来的空间智能应用，长期持续的同步定位与地图构建（SLAM）受到广泛关注。SLAM通常由具备感知与通信能力的移动机器人实现。本文针对长期SLAM中机器人的能耗问题，综合考虑感知、通信及运动因素，建立基于2D激光雷达（LiDAR）与里程计的系统模型。原始点云数据与里程计信息通过无线方式传输至数据中心，采用无监督深度学习方法进行实时地图重建。通过联合优化感知时长、发射功率、传输时长与探索速度，实现能耗最小化。仿真与实验验证了所提方法的有效性。
原文摘要 · Abstract (English)
To support future spatial machine intelligence applications, lifelong simultaneous localization and mapping (SLAM) has drawn significant attentions. SLAM is usually realized based on various types of mobile robots performing simultaneous and continuous sensing and communication. This paper focuses on analyzing the energy efficiency of robot operation for lifelong SLAM by jointly considering sensing, communication and mechanical factors. The system model is built based on a robot equipped with a 2D light detection and ranging (LiDAR) and an odometry. The cloud point raw data as well as the odometry data are wirelessly transmitted to data center where real-time map reconstruction is realized based on an unsupervised deep learning based method. The sensing duration, transmit power, transmit duration and exploration speed are jointly optimized to minimize the energy consumption. Simulations and experiments demonstrate the performance of our proposed method.
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