融合激光与视觉，提升养鸡场机器人导航精度
The Composite Visual-Laser Navigation Method Applied in Indoor Poultry Farming Environments
- 动态融合激光与视觉数据，实时计算可靠航向角
- 无需物理导航线，在复杂光照与积水环境下仍稳定运行
- 适合需高精度巡检的智能养殖场景
室内养鸡场需依靠巡检机器人实现精准环境调控，以防止疾病快速传播和大规模禽类死亡。然而，养殖场内光照强烈、积水区域多，给导航带来挑战。传统单传感器方法常因激光漂移或视觉特征提取不准而失效。为此，本文提出一种新型复合导航方法，融合激光与视觉技术，根据各模态实时可靠性动态计算融合航向角，无需依赖物理导航线。在真实养鸡舍环境中验证表明，该方法有效克服了单一传感器系统的固有缺陷，显著提升导航精度与作业效率，为复杂室内养殖场景中的巡检机器人提供了可行解决方案。
原文摘要 · Abstract (English)
Indoor poultry farms require inspection robots to maintain precise environmental control, which is crucial for preventing the rapid spread of disease and large-scale bird mortality. However, the complex conditions within these facilities, characterized by areas of intense illumination and water accumulation, pose significant challenges. Traditional navigation methods that rely on a single sensor often perform poorly in such environments, resulting in issues like laser drift and inaccuracies in visual navigation line extraction. To overcome these limitations, we propose a novel composite navigation method that integrates both laser and vision technologies. This approach dynamically computes a fused yaw angle based on the real-time reliability of each sensor modality, thereby eliminating the need for physical navigation lines. Experimental validation in actual poultry house environments demonstrates that our method not only resolves the inherent drawbacks of single-sensor systems, but also significantly enhances navigation precision and operational efficiency. As such, it presents a promising solution for improving the performance of inspection robots in complex indoor poultry farming settings.
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