论文标题
部分可观测时空混沌系统的无模型预测
Overhead Image Factors for Underwater Sonar-based SLAM
论文作者
论文摘要
同时定位和映射（SLAM）是任何自动水下车辆（AUV）的关键能力。但是，使用低成本传感器时，稳健，准确的状态估计仍在进行中。我们建议使用广泛可用的和通常免费的先前信息来增强典型的低成本传感器套件；高架图像。鉴于AUV的声纳图像和部分重叠，全球引用的架空图像，我们建议使用卷积神经网络（CNN）生成合成的架空图像，以预测声纳图像内容的上面表面外观。然后，我们使用此综合架空图像将我们的观察结果注册到提供的全局架空图像。注册后，将转换作为姿势大满贯因子图引入。我们使用最先进的仿真环境对一系列基准轨迹进行验证，并定量地显示了使用该方法的机器人状态估计的准确性提高。我们还显示了真正的AUV现场部署的定性结果。视频附件：https：//youtu.be/_uwljtp58ks
Simultaneous localization and mapping (SLAM) is a critical capability for any autonomous underwater vehicle (AUV). However, robust, accurate state estimation is still a work in progress when using low-cost sensors. We propose enhancing a typical low-cost sensor package using widely available and often free prior information; overhead imagery. Given an AUV's sonar image and a partially overlapping, globally-referenced overhead image, we propose using a convolutional neural network (CNN) to generate a synthetic overhead image predicting the above-surface appearance of the sonar image contents. We then use this synthetic overhead image to register our observations to the provided global overhead image. Once registered, the transformation is introduced as a factor into a pose SLAM factor graph. We use a state-of-the-art simulation environment to perform validation over a series of benchmark trajectories and quantitatively show the improved accuracy of robot state estimation using the proposed approach. We also show qualitative outcomes from a real AUV field deployment. Video attachment: https://youtu.be/_uWljtp58ks
