Xiaofeng Han

Xiaofeng Han, Shunpeng Chen, Zenghuang Fu et al.

Robot vision has greatly benefited from advancements in multimodal fusion techniques and vision-language models (VLMs). We adopt a task-oriented perspective to systematically review the applications and advancements of multimodal fusion methods and VLMs in the field of robot vision. For semantic scene understanding tasks, we categorize fusion approaches into encoder-decoder frameworks, attention-based architectures, and graph neural networks. Meanwhile, we also analyze the architectural characteristics and practical implementations of these fusion strategies in key tasks such as simultaneous localization and mapping (SLAM), 3D object detection, navigation, and manipulation. We compare the evolutionary paths and applicability of VLMs based on large language models (LLMs) with traditional multimodal fusion methods.Additionally, we conduct an in-depth analysis of commonly used datasets, evaluating their applicability and challenges in real-world robotic scenarios. Building on this analysis, we identify key challenges in current research, including cross-modal alignment, efficient fusion, real-time deployment, and domain adaptation. We propose future directions such as self-supervised learning for robust multimodal representations, structured spatial memory and environment modeling to enhance spatial intelligence, and the integration of adversarial robustness and human feedback mechanisms to enable ethically aligned system deployment. Through a comprehensive review, comparative analysis, and forward-looking discussion, we provide a valuable reference for advancing multimodal perception and interaction in robotic vision. A comprehensive list of studies in this survey is available at https://github.com/Xiaofeng-Han-Res/MF-RV.

Xiaofeng Han, Amjed Tahir, Peng Liang et al.

Code review plays an important role in software quality control. A typical review process would involve a careful check of a piece of code in an attempt to find defects and other quality issues/violations. One type of issues that may impact the quality of the software is code smells - i.e., bad programming practices that may lead to defects or maintenance issues. Yet, little is known about the extent to which code smells are identified during code reviews. To investigate the concept behind code smells identified in code reviews and what actions reviewers suggest and developers take in response to the identified smells, we conducted an empirical study of code smells in code reviews using the two most active OpenStack projects (Nova and Neutron). We manually checked 19,146 review comments obtained by keywords search and random selection, and got 1,190 smell-related reviews to study the causes of code smells and actions taken against the identified smells. Our analysis found that 1) code smells were not commonly identified in code reviews, 2) smells were usually caused by violation of coding conventions, 3) reviewers usually provided constructive feedback, including fixing (refactoring) recommendations to help developers remove smells, and 4) developers generally followed those recommendations and actioned the changes. Our results suggest that 1) developers should closely follow coding conventions in their projects to avoid introducing code smells, and 2) review-based detection of code smells is perceived to be a trustworthy approach by developers, mainly because reviews are context-sensitive (as reviewers are more aware of the context of the code given that they are part of the project's development team).

Huafeng Liu, Xiaofeng Han, Xiangrui Li et al.

Robust road detection is a key challenge in safe autonomous driving. Recently, with the rapid development of 3D sensors, more and more researchers are trying to fuse information across different sensors to improve the performance of road detection. Although many successful works have been achieved in this field, methods for data fusion under deep learning framework is still an open problem. In this paper, we propose a Siamese deep neural network based on FCN-8s to detect road region. Our method uses data collected from a monocular color camera and a Velodyne-64 LiDAR sensor. We project the LiDAR point clouds onto the image plane to generate LiDAR images and feed them into one of the branches of the network. The RGB images are fed into another branch of our proposed network. The feature maps that these two branches extract in multiple scales are fused before each pooling layer, via padding additional fusion layers. Extensive experimental results on public dataset KITTI ROAD demonstrate the effectiveness of our proposed approach.