论文标题
在6 DOF中进行定位和密集的3D映射的变化状态空间模型
Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF
论文作者
论文摘要
我们解决了在空间环境中的6-DOF定位和3D密集重建的问题，作为在深度空间模型中的近似贝叶斯推断。我们的方法从多视图的几何形状和僵化的身体动力学中利用学习和领域知识。这导致了世界的表现力预测模型，通常在当前最新的视觉大满贯解决方案中缺少。变异推理，神经网络和可微分的射线卡机的组合确保了我们的模型可与基于端梯度的优化相吻合。我们评估了对现实的无人机飞行数据的方法，接近最先进的视觉持续频仪系统。我们演示了该模型对生成预测和计划的适用性。
We solve the problem of 6-DoF localisation and 3D dense reconstruction in spatial environments as approximate Bayesian inference in a deep state-space model. Our approach leverages both learning and domain knowledge from multiple-view geometry and rigid-body dynamics. This results in an expressive predictive model of the world, often missing in current state-of-the-art visual SLAM solutions. The combination of variational inference, neural networks and a differentiable raycaster ensures that our model is amenable to end-to-end gradient-based optimisation. We evaluate our approach on realistic unmanned aerial vehicle flight data, nearing the performance of state-of-the-art visual-inertial odometry systems. We demonstrate the applicability of the model to generative prediction and planning.
