论文标题
CNN授权的视觉惯性大满贯和平面约束
CNN-Augmented Visual-Inertial SLAM with Planar Constraints
论文作者
论文摘要
我们提出了一个强大的视觉惯性大满贯系统，该系统结合了卷积神经网络（CNN）和平面约束的好处。我们的系统利用CNN预测每个图像的深度图和相应的不确定性图。 CNN的深度有效地引导了SLAM的后端优化，同时CNN不确定性会自适应地权衡每个特征点对后端优化的贡献。鉴于惯性传感器的重力方向，我们进一步提出了一种快速平面检测方法，该方法通过一点点兰萨克和垂直平面通过两点兰萨克检测水平平面。这些稳定检测到的平面又用于使大满贯的后端优化正规化。我们在公共数据集（IE，EUROC）上评估了我们的系统，并在最先进的SLAM系统\ ie，Orb-slam3上展示了改进的结果。
We present a robust visual-inertial SLAM system that combines the benefits of Convolutional Neural Networks (CNNs) and planar constraints. Our system leverages a CNN to predict the depth map and the corresponding uncertainty map for each image. The CNN depth effectively bootstraps the back-end optimization of SLAM and meanwhile the CNN uncertainty adaptively weighs the contribution of each feature point to the back-end optimization. Given the gravity direction from the inertial sensor, we further present a fast plane detection method that detects horizontal planes via one-point RANSAC and vertical planes via two-point RANSAC. Those stably detected planes are in turn used to regularize the back-end optimization of SLAM. We evaluate our system on a public dataset, \ie, EuRoC, and demonstrate improved results over a state-of-the-art SLAM system, \ie, ORB-SLAM3.
