Infrared image helps improve the perception capabilities of autonomous driving in complex weather conditions such as fog, rain, and low light. However, infrared image often suffers from low contrast, especially in non-heat-emitting targets like bicycles, which significantly affects the performance of downstream high-level vision tasks. Furthermore, achieving contrast enhancement without amplifying noise and losing important information remains a challenge. To address these challenges, we propose a task-oriented infrared image enhancement method. Our approach consists of two key components: layer decomposition and saliency information extraction. First, we design an l0-l1layer decomposition method for infrared images, which enhances scene details while preserving dark region features, providing more features for subsequent saliency information extraction. Then, we propose a morphological reconstruction-based saliency extraction method that effectively extracts and enhances target information without amplifying noise. Our method improves the image quality for object detection and semantic segmentation tasks. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods.

The integration of RGB and thermal data can significantly improve semantic segmentation performance in wild environments for field robots. Nevertheless, multi-source data processing (e.g. Transformer-based approaches) imposes significant computational overhead, presenting challenges for resource-constrained systems. To resolve this critical limitation, we introduced CM-SSM, an efficient RGB-thermal semantic segmentation architecture leveraging a cross-modal state space modeling (SSM) approach. Our framework comprises two key components. First, we introduced a cross-modal 2D-selective-scan (CM-SS2D) module to establish SSM between RGB and thermal modalities, which constructs cross-modal visual sequences and derives hidden state representations of one modality from the other. Second, we developed a cross-modal state space association (CM-SSA) module that effectively integrates global associations from CM-SS2D with local spatial features extracted through convolutional operations. In contrast with Transformer-based approaches, CM-SSM achieves linear computational complexity with respect to image resolution. Experimental results show that CM-SSM achieves state-of-the-art performance on the CART dataset with fewer parameters and lower computational cost. Further experiments on the PST900 dataset demonstrate its generalizability. Codes are available at https://github.com/xiaodonguo/CMSSM.

RGB-thermal (RGB-T) semantic segmentation enables intelligent vehicles to understand environments while operating in urban scenes. However, the research encounters two main challenges: 1) scarcity of training samples under snowy conditions and 2) challenge in applying the model in practice. To address the first challenge, we proposed a publicly accessible RGB-T semantic segmentation dataset in snowy urban scenes (SUS dataset). The SUS dataset comprises 1035 pairs of precisely registered RGB-T images, and provides pixel-level semantic annotations for five categories for all images. To tackle the second challenge, we introduced MCNet-S*, a novel semantic segmentation model that leverages knowledge distillation (KD). The KD structure consists of an RGB-T teacher model, named MCNet-T, and an RGB student model, named MCNet-S. Within MCNet-T, we proposed a cross-modal dual association (CDA) module to enhance utilization of RGB-T information in snowy urban scenes. Within MCNet-S, a depth-wise separable pyramid (DSP) module was proposed to improve the efficiency of RGB information utilization and align the feature dimensions with those of MCNet-T. Between MCNet-S and MCNet-T, memory-based contrastive learning distillation (MCLD) was proposed to transfer the prior thermal knowledge, improving the segmentation accuracy of MCNet-S and obtaining optimized MCNet-S*. Extensive experiments on the SUS and MFNet datasets show that the proposed models outperform state-of-the-art models. The SUS dataset and codes are available at https://github.com/xiaodonguo/SUS_dataset.

Point cloud registration is a crucial technology in the fields of robotics and computer vision. Despite the significant advances in point cloud registration enabled by Transformer-based methods, limitations persist due to indistinct feature extraction, noise sensitivity, and outlier handling. These limitations stem from three factors: (1) the inefficiency of convolutional neural networks (CNNs) to capture global relationships due to their local receptive fields, resulting in extracted features susceptible to noise; (2) the shallow-wide architecture of Transformers, coupled with a lack of positional information, leading to inefficient information interaction and indistinct feature extraction; and (3) the omission of geometrical compatibility leads to ambiguous identification of incorrect correspondences. To overcome these limitations, we propose the Deep Interactive Full Transformer (DIFT) network for point cloud registration, which consists of three key components: (1) a Point Cloud Structure Extractor (PSE) for modeling global relationships and retrieving structural information; (2) a Point Feature Transformer (PFT) for establishing comprehensive associations and directly learning the relative positions between points; and (3) a Geometric Matching-based Correspondence Confidence Evaluation (GMCCE) method for measuring spatial consistency and estimating correspondence confidence. Experimental results on ModelNet40 and 3DMatch datasets demonstrate the superior performance of our proposed method compared to existing state-of-the-art methods. The code for our method is publicly available at https://github.com/CGuangyan-BIT/DIFT.

The detection and tracking of dynamic traffic participants (e.g., pedestrians, cars, and bicyclists) plays an important role in reliable decision-making and intelligent navigation for autonomous vehicles. However, due to the rapid movement of the target, most current vision-based tracking methods, which perform tracking in the image domain or invoke 3D information in parts of their pipeline, have real-life limitations such as lack of the ability to recover tracking after the target is lost. In this work, we overcome such limitations and propose a complete system for dynamic object tracking in 3D space that combines: (1) a 3D position tracking algorithm based on monocular camera and LIDAR for the dynamic object; (2) a re-tracking mechanism (RTM) that restore tracking when the target reappears in camera's field of view. Compared with the existing methods, each sensor in our method is capable of performing its role to preserve reliability, and further extending its functions through a novel multimodality fusion module. We perform experiments in the real-world self-driving environment and achieve a desired 10Hz update rate for real-time performance. Our quantitative and qualitative analysis shows that this system is reliable for dynamic object tracking purposes of self-driving cars.

Nowadays, deep learning techniques are widely used for lane detection, but application in low-light conditions remains a challenge until this day. Although multi-task learning and contextual-information-based methods have been proposed to solve the problem, they either require additional manual annotations or introduce extra inference overhead respectively. In this paper, we propose a style-transfer-based data enhancement method, which uses Generative Adversarial Networks (GANs) to generate images in low-light conditions, that increases the environmental adaptability of the lane detector. Our solution consists of three parts: the proposed SIM-CycleGAN, light conditions style transfer and lane detection network. It does not require additional manual annotations nor extra inference overhead. We validated our methods on the lane detection benchmark CULane using ERFNet. Empirically, lane detection model trained using our method demonstrated adaptability in low-light conditions and robustness in complex scenarios. Our code for this paper will be publicly available.

This paper proposes a new type of spherical wheeled robot with an annular support leg. It can keep statically stable when powered off and automatically stand up with the assistance of the support leg when powered on. The stability of the robot at equilibrium is verified firstly using the planar simplified model. And the robot is proved to be controllable. Thus a double-closed loop control system is designed to stabilize the robot. Based on it, the standing-up control system and ramp-climbing control system are realized by changing control structure and using fuzzy control strategies. The design of the annular support leg, the experimental results and conclusions are also described in this paper.