语义视觉同时定位和映射 (SLAM) 使用深度学习用于动态场景
Xiao Ya Zhang1, Abdul Hadi Abd Rahman1, Faizan Qamar2
1Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia.
PeerJ. Computer science
|October 23, 2023
概括
这项研究增强了动态环境中的机器人的同时本地化和映射 (SLAM). 通过使用语义细分来移除移动的物体,它显著提高了姿势估计的准确性和稳定性.
科学领域:
- 机器人和计算机视觉 机器人和计算机视觉
- 人工智能的人工智能
- 自主系统 自主系统
背景情况:
- 同时定位和映射 (SLAM) 对于机器人在未知环境中的导航至关重要.
- 动态环境对SLAM准确性和稳定性构成重大挑战.
- 传统的方法难以可靠地区分静态和动态对象.
研究的目的:
- 在动态环境中增强单眼视觉测距仪的稳定性和精度.
- 通过解决动态对象干扰来提高同时定位和映射 (SLAM) 系统性能.
主要方法:
- 使用DeepLabV3+进行语义细分来识别动态对象.
- 实现了运动一致性检查,以过出动态特征点.
- 应用ORB-SLAM2以过的静态特征用于TUM数据集上的姿势估计.
主要成果:
- 拟议的方法在动态环境中明显优于传统的视觉测距.
- 通过消除移动物体的影响,证明了提高准确性和稳定性.
- 与ORB-SLAM2相比,实现了绝对轨迹误差 (ATE) 和相对姿势误差 (RPE) 的大幅降低.
结论:
- 增强的SLAM方法有效地处理动态环境,提高姿势估计的准确性.
- 语义细分和运动一致性检查对于强大的机器人导航至关重要.
- 这种方法为在复杂,不断变化的场景中可靠的自主系统运行提供了显著的进步.
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