Energy-Efficient SLAM via Joint Design of Sensing, Communication, and Exploration Speed

TL;DR

This paper proposes an energy-efficient approach for lifelong SLAM by jointly optimizing sensing, communication, and exploration speed in mobile robots, supported by simulations and experiments.

Contribution

It introduces a joint optimization framework for sensing, communication, and exploration speed to enhance energy efficiency in lifelong SLAM systems.

Findings

Optimized sensing and communication parameters reduce energy consumption.

Joint design improves SLAM system efficiency in real-world experiments.

Simulation results validate the effectiveness of the proposed method.

Abstract

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.