Semisupervised Depth Estimation from Sparse Depth and a Single Image for Dense Map Construction For robust navigation, the objective in visual SLAM is to create a dense map from a sparse input. Although there have been a significant number of endeavors on realtime mapping, the existing works for visual SLAM systems still fail to preserve adequate geometry details that are important for navigation. This paper estimates pixelwise depth from a single image and a few depth points which are constructed from registered LiDAR or acquired from visual SLAM systems to construct a dense map. The main idea is to employ a set of new loss functions consisting of photometric reconstruction consistency (forwardbackward consistency and leftright consistency), depth loss, nearby frame geometric consistency, and smoothness loss and propose a depth estimation network based on ResNet. The experimental results show that the proposed method is superior the stateoftheart methods on both raw LiDAR scans dataset and semidense annotation dataset. Furthermore, the errors of the sparse depth produced by stereo ORBSLAM2 are evaluated and this sparse depth and a single image are fed into the proposed model to further demonstrate the superiority of the proposed work.

SemiSupervised Monocular Depth Estimation with LeftRight Consistency Using Deep Neural Network There has been tremendous research progress in estimating the depth of a scene from a monocular camera image. Existing methods for singleimage depth prediction are exclusively based on deep neural networks, and their training can be unsupervised using stereo image pairs, supervised using LiDAR point clouds, or semisupervised using both stereo and LiDAR. In general, semisupervised training is preferred as it does not suffer from the weaknesses of either supervised training, resulting from the difference in the cameras and the LiDARs field of view, or unsupervised training, resulting from the poor depth accuracy that can be recovered from a stereo pair. In this paper, we present our research in singleimage depth prediction using semisupervised training that outperforms the stateoftheart. We achieve this through a loss function that explicitly exploits leftright consistency in a stereo reconstruction, which has not been adopted in previous semisupervised training. In addition, we describe the correct use of ground truth depth derived from LiDAR that can significantly reduce prediction error. The performance of our depth prediction model is evaluated on popular datasets, and the importance of each aspect of our semisupervised training approach is demonstrated through experimental results. Our deep neural network model has been made publicly available.1.