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Zhenping Sun

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4 papers

1 author row

IROS Conference 2025 Conference Paper

ETA: Learning Optical Flow with Efficient Temporal Attention

Bo Wang 0144
Zhenping Sun
Yang Yu 0014
Li Liu 0002
Jian Li 0003
Dewen Hu

Considering the potential of using multi-frame information to solve the occlusion problem, we introduce a novel idea of multi-frame information integration, which uses the attention mechanism to fuse the temporal information from the previous frame. The idea can effectively improve the estimation accuracy in occluded regions and optimize the inference speed under multi-frame settings. Meanwhile, we suggest the concept of attention confidence to provide an explicit value criterion for the model to utilize useful attention information more efficiently. Furthermore, we propose an Efficient Temporal Attention network (ETA), which achieves promising results on Sintel and KITTI benchmarks, especially with a 9. 4% error reduction compared to the baseline method GMA on Sintel (test) Clean.

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IROS Conference 2024 Conference Paper

Efficient-PIP: Large-scale Pixel-level Aligned Image Pair Generation for Cross-time Infrared-RGB Translation

Jian Li 0003
Kexin Fei
Yi Sun
Jie Wang
Bokai Liu
Zongtan Zhou
Yongbin Zheng
Zhenping Sun

Generative models are gaining momentum in both academic and industrial applications driven by the availability of large-scale datasets, especially in tasks involving Image-to-Image Translation. Meanwhile, poor human perception of nighttime environment has led to a demand for translation from night-vision infrared to day-vision RGB images. However, collecting such cross-modal training data at the same time is impossible due to the thermal imaging properties of infrared cameras, the challenge lies in constructing image pairs during the day and at night respectively, where the requirement for data alignment poses significant difficulties. In this paper, we propose a Pixel-level aligned Image Pair generation framework PIP to explore efficient colorization of high-resolution infrared images. Specifically, we first construct a 3D high-precision point cloud map for the purpose of establishing the correlation between day and night scenes. Corresponding point clouds of modal images are collected simultaneously during data acquisition to obtain image sensor poses by Global Matching with the map, which allows us to calculate the transformation relationship from infrared to RGB image coordinate systems based on the sensor parameters and depth information of the map. Leveraging the relationship, the pixel values of RGB image is projected onto the infrared image followed by optimization as the colored image. Accordingly, we present a dataset NUDT-PIP, the first of its kind containing large-scale pixel-level aligned cross-time infrared-RGB image pairs of complicated real road scenes. Experimental results demonstrate the reliability and strong applicability of our dataset in Image-to-Image Translation. Our code will be released at https://github.com/wjjjjyourFA/NUDT-PIP.

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IROS Conference 2024 Conference Paper

M3-GMN: A Multi-environment, Multi-LiDAR, Multi-task dataset for Grid Map based Navigation

Guanglei Xie
Hao Fu 0001
Hanzhang Xue
Bokai Liu
Xin Xu 0001
Xiaohui Li 0007
Zhenping Sun

In this paper, we propose a multi-environment, multi-LiDAR, multi-task dataset to promote the grid map-based navigation capability for autonomous vehicles. The dataset comprises structured and unstructured environmental data captured by different types of LiDAR and contains various challenging scenarios, including moving objects, negative obstacles, steep slopes, cliffs, overhangs, etc. Further, we have devised an innovative method for generating ground truth, facilitating the creation of dense, accurate, and stable grid maps with a minimal requirement for human annotation efforts. A new baseline method and two existing approaches are evaluated on this dataset. Results indicate that existing approaches perform much worse than the proposed baseline. The dataset will be made publicly available at https://github.com/guanglei96/M3-GMN.

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IROS Conference 2012 Conference Paper

Ribbon Model based path tracking method for autonomous land vehicle

Zhenping Sun
Qingyang Chen
Yiming Nie
Daxue Liu
Hangen He

To address the path tracking problem of autonomous land vehicle, a new vehicle-road model named “Ribbon Model” is constructed under the constraints of road width and vehicle geometry structure. A new vehicle-road evaluation algorithm is developed based on this model, and new path tracking controller is designed. The difficulties of preview distance selection and parameters tuning with speed of pure following controller are avoided in this controller. Performance of the novel method is verified by simulation and vehicle experiments.

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