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首页> 外文会议>International Conference on Pattern Recognition >Extending Single Beam Lidar To Full Resolution By Fusing with Single Image Depth Estimation
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Extending Single Beam Lidar To Full Resolution By Fusing with Single Image Depth Estimation

机译：通过熔断单个图像深度估计将单束立激达延伸到全分辨率

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摘要

Depth estimation is playing an important role in indoor and outdoor scene understanding, autonomous driving, augmented reality and many other tasks. Vehicles and robotics are able to use active illumination sensors such as LIDAR to receive high precision depth estimation. However, high-resolution LIDARs are usually too expensive, which limits its massive production on various applications. Though single beam LIDAR enjoys the benefits of low cost, one beam depth sensing is not usually sufficient to perceive the surrounding environment in many scenarios. In this paper, we propose a deep learning based framework to explore to replicate similar or even higher performance as costly LIDARs with our designed self-supervised network and a low-cost single-beam LIDAR. After the accurate calibration with a visible camera, the single beam LIDAR can adjust the scale uncertainty of the depth map estimated by the visible camera. The adjusted depth map enjoys the benefits of high resolution and sensing accuracy as high beam LIDAR and maintains low-cost as single beam LIDAR. Thus we can achieve similar sensing effect of high beam LIDAR with more than a 30–100 times cheaper price (e.g., $80000 Velodyne HDL-64E LIDAR v.s. $2000 SICK TIM-781 2D LIDAR and normal camera). The proposed approach is verified on our collected dataset and public dataset with superior depth-sensing performance.
机译：深度估计在室内和户外场景的理解，自动驾驶，增强现实以及许多其他任务中发挥着重要作用。车辆和机器人能够使用如LIDAR等主动照明传感器来接收高精度深度估计。然而，高分辨率的亮度通常太昂贵，这限制了其在各种应用上的大量生产。虽然单束闪光灯享受低成本的好处，但是一个光束深度感测通常不足以在许多情况下感知周围环境。在本文中，我们提出了一种深入的学习框架，探索与我们设计的自我监督网络和低成本单束立激达的昂贵的Lidars复制类似或甚至更高的性能。在用可见摄像机进行准确校准后，单束LIDAR可以调整可见相机估计的深度图的规模不确定性。调整后深度图享有高分辨率和感测精度的优点，作为高光束延线，并将低成本保持为单束立峰。因此，我们可以实现高光束激光雷达的类似感测效果，便宜30-100倍以下（例如，80000美元的Velodyne HDL-64E Lidar V.S. $ 2000生病的Tim-781 2D LIDAR和普通摄像机）。在我们收集的数据集和公共数据集中验证了所提出的方法，具有卓越的深度感测性能。

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