一个轻量级的,集中式的,协作式的,截断式的,基于距离功能的,为多个移动车辆提供密集的同时定位和映射系统
Haohua Que1, Haojia Gao2, Weihao Shan3
1College of Science, Beijing Forestry University, Beijing 100083, China.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
本研究介绍了一种高效的多机器人密集的同时定位和映射 (SLAM) 系统,以克服大型勘探中的机器人局限性. 这种新的方法降低了计算负载,使实时密集映射和自主机器人的精确定位成为可能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 同时定位和映射 (SLAM) 对于移动机器人在未知环境中的自主性至关重要.
- 移动机器人的资源限制限制了它们长期,大规模的勘探任务的能力.
- 现有的SLAM系统经常与密集映射的计算和内存需求作斗争.
研究的目的:
- 提出一个高效的多机器人密集SLAM系统,以解决单个移动机器人的局限性.
- 通过集中式结构来减少单个机器人的计算和内存负担.
- 为了实现实时密集映射和准确的定位,以增强自主探索.
主要方法:
- 一个集中式的多机器人系统架构来分配计算负载.
- 一种轻量级和准确的密集映射方法,用于实时代理级映射.
- 一种新的循环关闭检测方法,使用视觉和密集的几何信息.
- 多机器人姿势图形优化,整合密集的几何和轨迹数据.
主要成果:
- 通过对预先记录的数据集进行实验来证明系统的效率和准确性.
- 实现了现实世界的密集映射更新率,大约为每秒14.
- 在Jetson Xavier NX.上报告了低的内置映射RAM使用率 (~3.4%) 和带宽消耗 (~302 KB/s).
结论:
- 提出的高效的多机器人密集SLAM系统有效地减轻移动机器人的计算和内存负担.
- 该系统可实现实时密集映射和准确的多机器人定位,用于大规模的自主勘探.
- 该方法对在危险或难以进入的环境中实际部署具有重大前景.
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