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arXiv · 2205.02940

CNN-Augmented Visual-Inertial SLAM with Planar Constraints

Pan Ji·Yuan Tian·Qingan Yan·Yuxin Ma·Yi Xu
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Abstract

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.

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Pan Ji, Yuan Tian, Qingan Yan, Yuxin Ma, Yi Xu. 2022-05-05. CNN-Augmented Visual-Inertial SLAM with Planar Constraints. https://arxiv.org/abs/2205.02940

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