Multi-map Sparse Monocular visual Simultaneous Localization and Mapping applied to monocular endoscopic sequences has proven efficient to robustly recover tracking after the frequent losses in endoscopy due to motion blur, temporal occlusion, tools interaction or water jets. The sparse multi-maps are adequate for robust camera localization, however they are very poor for environment representation, they are noisy, with a high percentage of inaccurately reconstructed 3D points, including significant outliers, and more importantly with an unacceptable low density for clinical applications. We propose a method to remove outliers and densify the maps of the state of the art for sparse endoscopy multi-map CudaSIFT-SLAM. The NN LightDepth for up-to-scale depth dense predictions are aligned with the sparse CudaSIFT submaps by means of the robust to spurious LMedS. Our system mitigates the inherent scale ambiguity in monocular depth estimation while filtering outliers, leading to reliable densified 3D maps. We provide experimental evidence of accurate densified maps 4.15 mm RMS accuracy at affordable computing time in the C3VD phantom colon dataset. We report qualitative results on the real colonoscopy from the Endomapper dataset.

翻译：应用于单目内窥镜序列的多地图稀疏单目视觉同时定位与建图技术，已被证明能有效应对内窥镜检查中因运动模糊、瞬时遮挡、器械交互或水流喷射导致的频繁跟踪丢失问题，实现鲁棒的跟踪恢复。稀疏多地图虽足以支持稳健的相机定位，但其环境表征能力严重不足：存在大量噪声、高比例的重建不准确三维点（包含显著异常值），且最关键的是其点云密度远低于临床应用的可接受标准。本文提出一种针对当前先进的稀疏内窥镜多地图系统CudaSIFT-SLAM的异常值剔除与地图稠密化方法。通过抗伪值LMedS方法，将用于尺度化稠密深度预测的NN LightDepth网络与稀疏CudaSIFT子地图进行对齐。该系统在滤除异常值的同时缓解了单目深度估计固有的尺度歧义问题，从而生成可靠的三维稠密地图。我们在C3VD仿真结肠数据集上提供了实验证据，表明所生成稠密地图的均方根精度达到4.15毫米，且计算耗时可控。同时，我们在Endomapper数据集的实际结肠镜序列上报告了定性评估结果。