用廉价摄像头在极暗环境下实现稳定定位，突破夜间视觉局限。
Long Exposure Localization in Darkness Using Consumer Cameras
- 通过长曝光生成模糊图像，利用序贯匹配算法实现夜间定位
- 10秒曝光下仍能完成跨昼夜定位，误差率低于15%
- 提出图像归一化策略，提升极端光照变化下的匹配鲁棒性
本文评估了SeqSLAM算法在极暗环境中的被动视觉定位性能，使用低成本摄像头在移动车辆中实现长达10,000毫秒的曝光，导致图像严重模糊。研究对比了白天与夜间学习路径的定位效果，验证了在两种不同环境下的跨时段定位可行性。进一步开展统计分析，比较原始灰度图像匹配与采用块归一化及局部邻域归一化处理的效果，揭示了该算法有效性的内在机制。结果首次阐明了SeqSLAM在极端外观变化下的工作原理，展示了低成本摄像头系统在恶劣光照条件下实现可靠定位的潜力。
原文摘要 · Abstract (English)
In this paper we evaluate performance of the SeqSLAM algorithm for passive vision-based localization in very dark environments with low-cost cameras that result in massively blurred images. We evaluate the effect of motion blur from exposure times up to 10,000 ms from a moving car, and the performance of localization in day time from routes learned at night in two different environments. Finally we perform a statistical analysis that compares the baseline performance of matching unprocessed grayscale images to using patch normalization and local neighborhood normalization - the two key SeqSLAM components. Our results and analysis show for the first time why the SeqSLAM algorithm is effective, and demonstrate the potential for cheap camera-based localization systems that function despite extreme appearance change.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。