IEEE Trans Vis Comput Graph. 2017 Nov;23(11):2389-2398. doi: 10.1109/TVCG.2017.2734458. Epub 2017 Aug 10.
Visual SLAM is one of the key technologies to align the virtual and real world together in Augmented Reality applications. RGBD dense Visual SLAM approaches have shown their advantages in robustness and accuracy in recent years. However, there are still several challenges such as the inconsistencies in RGBD measurements across multiple frames that could jeopardize the accuracy of both camera trajectory and scene reconstruction. In this paper, we propose a novel map representation called Probabilistic Surfel Map (PSM) for dense visual SLAM. The main idea is to maintain a globally consistent map with both photometric and geometric uncertainties encoded in order to address the inconsistency issue. The key of our PSM is proper modeling and updating of sensor measurement uncertainties, as well as the strategies to apply them for improving both the front-end pose estimation and the back-end optimization. Experimental results on publicly available datasets demonstrate major improvements with our approach over the state-of-the-art methods. Specifically, comparing with σ-DVO, we achieve a 40% reduction in absolute trajectory error and an 18% reduction in relative pose error in visual odometry, as well as an 8.5% reduction in absolute trajectory error in complete SLAM. Moreover, our PSM enables generation of a high quality dense point cloud with comparable accuracy as the state-of-the-art approach.
视觉 SLAM 是将虚拟世界和现实世界对齐的关键技术之一，在增强现实应用中。近年来，RGBD 密集视觉 SLAM 方法在鲁棒性和准确性方面表现出了优势。然而，仍然存在一些挑战，例如在多个帧中 RGBD 测量之间的不一致性，这可能会危及相机轨迹和场景重建的准确性。在本文中，我们提出了一种新的地图表示方法，称为概率体素地图 (PSM)，用于密集视觉 SLAM。主要思想是保持全局一致的地图，同时编码光度和几何不确定性，以解决不一致性问题。我们的 PSM 的关键是正确建模和更新传感器测量不确定性，以及应用这些不确定性的策略，以提高前端姿态估计和后端优化的性能。在公开数据集上的实验结果表明，我们的方法在现有方法的基础上取得了重大改进。具体来说，与 σ-DVO 相比，我们在视觉里程计中实现了绝对轨迹误差减少 40%，相对姿态误差减少 18%，在完整 SLAM 中绝对轨迹误差减少 8.5%。此外，我们的 PSM 能够生成具有与最新方法相当的准确性的高质量密集点云。
