具有体积占据建图的不确定性感知视觉-惯性SLAM
Uncertainty-Aware Visual-Inertial SLAM with Volumetric Occupancy Mapping
AI总结：
本文提出一种不确定性感知的视觉-惯性SLAM，通过概率化融合深度网络预测与运动立体视觉，将深度不确定性传播到体积占据建图和子图对齐中，在基准数据集上实现超越现有方法的定位与建图精度，并实时输出可用于机器人规划控制的体积占据信息。
摘要：
我们提出了一种视觉-惯性同时定位与建图方法，该方法将稀疏重投影误差、惯性测量单元预积分和相对位姿因子与稠密体积占据建图紧密耦合。在此过程中，来自深度神经网络的深度预测以完全概率化的方式进行融合。具体而言，我们的方法严格具备不确定性感知能力：首先，我们不仅使用来自机器人立体视觉装置的深度和不确定性预测，还进一步概率化地融合运动立体视觉，后者可在一系列基线范围内提供深度信息，从而大幅提升建图精度。其次，预测和融合后的深度不确定性不仅传播到占据概率中，还传播到生成稠密子图之间的对齐因子中，这些因子进入概率非线性最小二乘估计器。这种子图表示在大规模场景下提供全局一致的几何结构。我们的方法在两个基准数据集上进行了充分评估，其定位与建图精度超越了当前最先进水平，同时实时提供可直接用于下游机器人规划与控制的体积占据信息。
英文摘要：
We propose visual-inertial simultaneous localization and mapping that tightly couples sparse reprojection errors, inertial measurement unit pre-integrals, and relative pose factors with dense volumetric occupancy mapping. Hereby depth predictions from a deep neural network are fused in a fully probabilistic manner. Specifically, our method is rigorously uncertainty-aware: first, we use depth and uncertainty predictions from a deep network not only from the robot's stereo rig, but we further probabilistically fuse motion stereo that provides depth information across a range of baselines, therefore drastically increasing mapping accuracy. Next, predicted and fused depth uncertainty propagates not only into occupancy probabilities but also into alignment factors between generated dense submaps that enter the probabilistic nonlinear least squares estimator. This submap representation offers globally consistent geometry at scale. Our method is thoroughly evaluated in two benchmark datasets, resulting in localization and mapping accuracy that exceeds the state of the art, while simultaneously offering volumetric occupancy directly usable for downstream robotic planning and control in real-time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
