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.

翻译：为支持未来空间机器智能应用，终身同步定位与建图技术已引起广泛关注。SLAM通常通过各类移动机器人实现同步持续的感知与通信。本文通过联合考量感知、通信与机械因素，重点分析机器人执行终身SLAM任务的能效问题。系统模型基于搭载二维激光雷达与里程计的机器人构建。点云原始数据及里程计数据通过无线传输至数据中心，并采用基于无监督深度学习的方法实现实时地图重建。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性能表现。