单眼对象级SLAM通过联合语义分割和深度估计来增强
Ruicheng Gao1, Yue Qi1,2
1State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究介绍了一种单眼对象级SLAM系统,该系统使用联合深度估计和语义细分. 小说JSDNet改善了移动机器人和增强现实的动态场景中的规模模糊性和跟踪.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
背景情况:
- 同时定位和映射 (SLAM) 对移动机器人和增强现实 (AR) 至关重要.
- 单眼SLAM系统面临规模模糊性和仅使用RGB图像在动态环境中跟踪的挑战.
- 整合语义信息可以通过模仿人类视觉感知来增强SLAM.
研究的目的:
- 开发一个单眼对象级SLAM系统,克服传统的RGB-based方法的局限性.
- 通过结合实时关节深度估计和语义细分来提高SLAM性能.
- 为了提高尺度确定和动态场景跟踪的准确性和稳定性.
主要方法:
- 提出了JSDNet,一个用于同时深度估计和语义细分的多任务网络.
- 在JSDNet中实现了功能融合和语义一致性优化.
- 开发了一个对象级SLAM系统,集成了像素级和对象级语义.
- 集成了一个尺度恢复过程,用于准确的尺度评估.
主要成果:
- 在NYU深度v2数据集上,JSDNet实现了最先进的深度估计和实时高分段精度.
- 与现有的SLAM系统相比,对象级SLAM系统在TUM RGB-D数据集上显示了较少的轨迹错误.
- 该系统有效地解决了规模模糊性,并在具有挑战性的场景中改进了跟踪.
结论:
- 拟议的单眼对象级SLAM系统显著增强了本地化和映射能力.
- 联合深度估计和语义细分为SLAM提供了强大的语义理解.
- 该系统为移动机器人和AR的实时应用提供了有前途的解决方案.
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