Simultaneous Localization and Mapping (SLAM) systems typically assume static, distant illumination; however, many real-world scenarios, such as endoscopy, subterranean robotics, and search & rescue in collapsed environments, require agents to operate with a co-located light and camera in the absence of external lighting. In such cases, dynamic near-field lighting introduces strong, view-dependent shading that significantly degrades SLAM performance. We introduce Near-Field Lighting Bundle Adjustment Loss (NFL-BA) which explicitly models near-field lighting as a part of Bundle Adjustment loss and enables better performance for scenes captured with dynamic lighting. NFL-BA can be integrated into neural rendering-based SLAM systems with implicit or explicit scene representations. Our evaluations mainly focus on endoscopy procedure where SLAM can enable autonomous navigation, guidance to unsurveyed regions, blindspot detections, and 3D visualizations, which can significantly improve patient outcomes and endoscopy experience for both physicians and patients. Replacing Photometric Bundle Adjustment loss of SLAM systems with NFL-BA leads to significant improvement in camera tracking, 37% for MonoGS and 14% for EndoGS, and leads to state-of-the-art camera tracking and mapping performance on the C3VD colonoscopy dataset. Further evaluation on indoor scenes captured with phone camera with flashlight turned on, also demonstrate significant improvement in SLAM performance due to NFL-BA. See results at https://asdunnbe.github.io/NFL-BA/

翻译：同步定位与建图（SLAM）系统通常假设光照为静态且来自远处；然而，在许多现实场景中，例如内窥镜检查、地下机器人作业以及坍塌环境下的搜救任务，智能体需要在缺乏外部光照的条件下，使用共置的光源与相机进行操作。在此类情况下，动态的近场光照会引入强烈的视角相关着色效应，显著降低SLAM的性能。我们提出了近场光照光束法平差损失（NFL-BA），该方法将近场光照显式建模为光束法平差损失的一部分，从而在动态光照捕获的场景中实现更优的性能。NFL-BA可以集成到基于神经渲染的SLAM系统中，无论其采用隐式还是显式的场景表示。我们的评估主要聚焦于内窥镜手术场景，其中SLAM能够实现自主导航、引导至未探查区域、盲点检测以及三维可视化，从而显著改善患者预后并提升医生与患者的内窥镜检查体验。将SLAM系统中的光度光束法平差损失替换为NFL-BA，可显著提升相机跟踪性能（在MonoGS上提升37%，在EndoGS上提升14%），并在C3VD结肠镜数据集上实现了最先进的相机跟踪与建图性能。进一步对使用手机闪光灯拍摄的室内场景进行评估，同样证明了NFL-BA带来的SLAM性能显著提升。结果请参见 https://asdunnbe.github.io/NFL-BA/