Self-driving cars have experienced rapid development in the past few years, and Simultaneous Localization and Mapping (SLAM) is considered to be their basic capabilities. In this article, we propose a direct vision LiDAR fusion SLAM framework that consists of three modules. Firstly, a two-staged direct visual odometry module, which consists of a frame-to-frame tracking step, and an improved sliding window based thinning step, is proposed to estimate the accurate pose of the camera while maintaining efficiency. Secondly, every time a keyframe is generated, a dynamic objects considered LiDAR mapping module is utilized to refine the pose of the keyframe to obtain higher positioning accuracy and better robustness. Finally, a Parallel Global and Local Search Loop Closure Detection (PGLS-LCD) module that combines visual Bag of Words (BoW) and LiDAR-Iris feature is applied for place recognition to correct the accumulated drift and maintain a globally consistent map. We conducted a large number of experiments on the public dataset and our mobile robot dataset to verify the effectiveness of each module in our framework. Experimental results show that the proposed algorithm achieves more accurate pose estimation than the state-of-the-art methods.

Different from object detection in natural image, optical remote sensing object detection is a challenging task, due to the diverse meteorological conditions, complex background, varied orientations, scale variations, etc. In this paper, to address this issue, we propose a novel object detection network (the global-local saliency constraint network, GLS-Net) that can make full use of the global semantic information and achieve more accurate oriented bounding boxes. More precisely, to improve the quality of the region proposals and bounding boxes, we first propose a saliency pyramid which combines a saliency algorithm with a feature pyramid network, to reduce the impact of complex background. Based on the saliency pyramid, we then propose a global attention module branch to enhance the semantic connection between the target and the global scenario. A fast feature fusion strategy is also used to combine the local object information based on the saliency pyramid with the global semantic information optimized by the attention mechanism. Finally, we use an angle-sensitive intersection over union (IoU) method to obtain a more accurate five-parameter representation of the oriented bounding boxes. Experiments with a publicly available object detection dataset for aerial images demonstrate that the proposed GLS-Net achieves a state-of-the-art detection performance.

Deep learning approaches require enough training samples to perform well, but it is a challenge to collect enough real training data and label them manually. In this letter, we propose a practical framework for automatically generating content-rich synthetic images with ground-truth annotations. By rendering 3-D CAD models, we generate two synthetic aircraft image data sets with wide distribution (Syn N and Syn U). For improving the quality of synthetic images, we propose a multiscale attention module which enhances the Cycle-Consistent Adversarial Network (CycleGAN) in spatial and channel dimensions. Then, we compare the synthetic images before and after translation qualitatively and quantitatively. Experiments on Northwestern Polytechnical University (NWPU) very high resolution (VHR)-10, University of Chinese Academy of Sciences, orientation robust object detection in aerial images (UCAS-AOD), and benchmark for object DetectIon in Optical Remote sensing images (DIOR) data sets demonstrate that synthetic data augmentation can improve the performance of aircraft detection in remote sensing images, especially when real data are insufficient. Synthetic data are available at: https://weix-liu.github.io/ .

An important means of remote sensing (RS) imagery interpretation, RS scene classification technology, has recently achieved great success, especially based on deep learning. However, most of these methods are designed for noise-free images. The scene classification performance for noisy RS images, i.e., synthetic aperture radar (SAR) images with speckle noise, is poor due to the sufficient effect of noise. An intuitive solution is denoising first and then classifying the image, which makes the whole process cumbersome. To address this problem, we design a new complete frequency channel attention network (CFCANet) that can handle noisy RS images directly without any filtering operation. CFCANet selects part of the low-frequency information to interact with the feature map adequately. For the original feature map, a corresponding 2-D discrete cosine transformation frequency component is assigned, from which the most significant eigenvalue of each channel is obtained by maximization. Compared with the frequency channel attention network (FcaNet), the proposed network has better antinoise ability as it exploits low frequency information of the images. The effectiveness of our method has been proved by experiments based on public datasets and some simulated datasets. Moreover, we build a new SAR scene classification dataset: WHU-SAR6. The comprehensive evaluation shows that the proposed method consistently outperforms several advanced methods, including ResNet, SENet, CBAM, EcaNet, and FcaNet.

Vehicle detection methods based on deep learning have achieved remarkable results on remote sensing images. However, the performance of the detector degrades when the test images are distinct from the training images. Domain adaptive vehicle detection is a promising approach to bridging the domain gap. Existing methods usually adopt fully shared networks, but ignore the problem that features from different domains may be incompatible within a single model. In this letter, we present a novel domain adaptive vehicle detection method based on patch-wise domain-specific channel recalibration (PDSCR). The PDSCR module routes the feature to the corresponding network branch and extracts the channel dependence using separate parameters. In this way, our method can explicitly capture domain-specific information for each domain. Furthermore, we propose a dynamic weighted prototype alignment (DWPA) to avoid the negative effects of false pseudo-labels, especially in the early stage of training. Experimental results of adaptation from our synthetic dataset to three real vehicle detection datasets demonstrate the effectiveness of our method. Code and our synthetic data will be available at https://weix-liu.github.io/.

There are large nonlinear radiance differences between optical and synthetic aperture radar (SAR) from different imaging sensors. When combined with rotational and scale variations, achieving equivariant matching becomes highly challenging. The existing single network models face two primary limitations, insufficient learning of modal invariant features and insufficient characterization of rotation and scale. After task decomposition, we propose a novel rotation and scale equivariant network (RSENet) for optical-SAR image matching, comprising two modules, SAR-to-optical image translation module and equivariant descriptors (ED). First, we propose an image translation network to obtain more beneficial pseudo-optical images for matching. Second, we achieve high-precision matching via equivariant descriptors designed through scale angle estimation (SAE) and descriptor colearning. The network initially corrects image blocks explicitly via scale and angle loss functions that are based on periodic encoding (PE) learning. Subsequently, it employs an attention module based on cross-scale perception and feature channel excitation to extract hierarchical information from the corrected image blocks and to enhance the local feature representation of the descriptors. We validate the effectiveness of RSENet by conducting numerous experiments under combined transformations of various angles and scales. Our proposed method achieves an approximately 28% improvement in the matching success rate compared with existing methods.