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Chengyang Li

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10 papers

2 author rows

AAAI Conference 2026 Conference Paper

NeuPAN: Direct Point Robot Navigation with End-to-End Model-Based Learning (Abstract Reprint)

Ruihua Han
Shuai Wang
Shuaijun Wang
Zeqing Zhang
Jianjun Chen
Shijie Lin
Chengyang Li
Chengzhong Xu

Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This article presents neural proximal alternating-minimization network (NeuPAN): a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: first, it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline; second, it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play proximal alternating-minimization network, incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via back propagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.

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IROS Conference 2025 Conference Paper

A Robust Stereo Splatting SLAM System with Inertial-Legged Fusion

Zuowei Chen
Yulai Zhang
Chengyang Li
Shengming Li
Toshio Fukuda
Qing Shi

Recent progress in stereo-based 3D Gaussian Splatting (3DGS) SLAM has enabled small-scale robots, which are too small to carry depth cameras, to achieve localization and reconstruct photorealistic scenes with high-speed rendering. However, initializing 3D Gaussians from binocular vision still requires further improvement, and the potential of robot proprioception has not been fully leveraged. This work presents a robust stereo 3DGS SLAM with efficient inertial-legged fusion for small-scale quadruped robots (SaQu-SLAM). We develop a light-weight network to densely initialize the 3D Gaussians in the space. Besides, an efficient fusion method of inertial and legged encoder data based on Kalman filter is introduced. To improve the cross-platform generalization of our algorithm, multiple configuration combinations of these three types of sensors are provided. Moreover, we propose a mode-switching mechanism to handle intermittent visual failures. At last, we perform evaluation on a benchmark dataset, which includes large- and small-scale scenes, and a small quadruped robot in real-world confined-scale scenes, reducing the absolute trajectory error by an average of 19%, 13% and 25% respectively, when compared with other state-of-the-art methods in a similar context. It is also the only successful method in our self-customized confined mixed textured and textureless scene, whereas all vision-based or visual-inertial methods fail. Our system achieves real-time performance even on an embedded platform (Jetson AGX Orin).

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IROS Conference 2024 Conference Paper

A Robust Visual SLAM System for Small-Scale Quadruped Robots in Dynamic Environments

Chengyang Li
Yulai Zhang
Zhiqiang Yu
Xinming Liu
Qing Shi

This paper presents a robust visual SLAM system designed for small-scale quadruped robots (ViQu-SLAM) for accurate localization, especially to mitigate the issue of erroneous data association caused by moving objects in dynamic environments. The proposed approach leverages a selfadaptive framework that integrates semantic segmentation with alterations in the spatial location of categorized map points. Besides, combination of leg odometry derived from forward kinematics with IMU provides scale information for positional transformations between keyframes, thus optimizing the overall localization accuracy of quadruped robots. At last, we performed evaluation across various stages and the results demonstrate competitive performance, with 53. 16% reduction in average absolute trajectory error compared to that of ORB-SLAM3 in dynamic benchmark datasets. As a result, ViQu-SLAM, including visual and IMU-fused leg odometry, exhibits promising results on a small quadruped robot, reducing positioning errors in dynamic scenes by an average of 29. 36% compared to existing state-of-the-art methods.

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AAAI Conference 2024 Conference Paper

From Toxic to Trustworthy: Using Self-Distillation and Semi-supervised Methods to Refine Neural Networks

Xianda Zhang
Baolin Zheng
Jianbao Hu
Chengyang Li
Xiaoying Bai

Despite the tremendous success of deep neural networks (DNNs) across various fields, their susceptibility to potential backdoor attacks seriously threatens their application security, particularly in safety-critical or security-sensitive ones. Given this growing threat, there is a pressing need for research into purging backdoors from DNNs. However, prior efforts on erasing backdoor triggers not only failed to withstand increasingly powerful attacks but also resulted in reduced model performance. In this paper, we propose From Toxic to Trustworthy (FTT), an innovative approach to eliminate backdoor triggers while simultaneously enhancing model accuracy. Following the stringent and practical assumption of limited availability of clean data, we introduce a self-attention distillation (SAD) method to remove the backdoor by aligning the shallow and deep parts of the network. Furthermore, we first devise a semi-supervised learning (SSL) method that leverages ubiquitous and available poisoned data to further purify backdoors and improve accuracy. Extensive experiments on various attacks and models have shown that our FTT can reduce the attack success rate from 97% to 1% and improve the accuracy of 4% on average, demonstrating its effectiveness in mitigating backdoor attacks and improving model performance. Compared to state-of-the-art (SOTA) methods, our FTT can reduce the attack success rate by 2 times and improve the accuracy by 5%, shedding light on backdoor cleansing.

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ICRA Conference 2024 Conference Paper

OmniColor: A Global Camera Pose Optimization Approach of LiDAR-360Camera Fusion for Colorizing Point Clouds

Bonan Liu
Guoyang Zhao
Jianhao Jiao
Guang Cai
Chengyang Li
Handi Yin
Yuyang Wang
Ming Liu 0001

A Colored point cloud, as a simple and efficient 3D representation, has many advantages in various fields, including robotic navigation and scene reconstruction. This representation is now commonly used in 3D reconstruction tasks relying on cameras and LiDARs. However, fusing data from these two types of sensors is poorly performed in many existing frameworks, leading to unsatisfactory mapping results, mainly due to inaccurate camera poses. This paper presents Omni-Color, a novel and efficient algorithm to colorize point clouds using an independent 360-degree camera. Given a LiDAR-based point cloud and a sequence of panorama images with initial coarse camera poses, our objective is to jointly optimize the poses of all frames for mapping images onto geometric reconstructions. Our pipeline works in an off-the-shelf manner that does not require any feature extraction or matching process. Instead, we find optimal poses by directly maximizing the photometric consistency of LiDAR maps. In experiments, we show that our method can overcome the severe visual distortion of omnidirectional images and greatly benefit from the wide field of view (FOV) of 360-degree cameras to reconstruct various scenarios with accuracy and stability. The code will be released at https://github.com/liubonan123/OmniColor/.

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EAAI Journal 2023 Journal Article

CBFLNet: Cross-boundary feature learning for large-scale point cloud segmentation

Liping Zhu
Cong Peng
Bingyao Wang
Chengyang Li
Kaijie Zhu

Large-scale point cloud semantic segmentation presents a crucial yet challenging task. Current point cloud analysis approaches typically partition data into volumetric blocks, independently assigning labels to each point within these blocks. However, this strategy often compromises segmentation accuracy at block boundaries due to the isolated processing of each block, thus hindering the model’s contextual understanding. To address this issue, we present CBFLNet, an innovative semantic segmentation model that enables offset-free feature upsampling and cross-boundary feature learning. CBFLNet facilitates interaction between adjacent blocks, extending its receptive field beyond the input block. As a result, it notably mitigates errors in block boundary segmentation. CBFLNet is composed of three modules: an explicit local representation module, a symmetric sampling module, and a cross-boundary feature fusion module. The explicit local representation module is a plug-and-play module that takes two overlapping point cloud blocks as input to construct an approximate local spatial representation. In the symmetric sampling module, point features are symmetrically downsampled and upsampled, effectively avoiding feature offset caused by interpolation. Lastly, the cross-boundary feature fusion module enables cross-boundary local feature learning and multi-scale feature fusion. CBFLNet demonstrates a significant performance improvement, achieving a 0. 9% mIoU increase over state-of-the-art methods on the S3DIS dataset and exhibiting competitive performance on the ScannetV2 dataset.

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IROS Conference 2023 Conference Paper

Decentralized Planning for Car-Like Robotic Swarm in Cluttered Environments

Changjia Ma
Zhichao Han 0002
Tingrui Zhang
Jingping Wang
Long Xu 0002
Chengyang Li
Chao Xu 0001
Fei Gao 0011

Robot swarm is a hot spot in robotic research community. In this paper, we propose a decentralized framework for car-like robotic swarm which is capable of real-time planning in cluttered environments. In this system, path finding is guided by environmental topology information to avoid frequent topological change, and search-based speed planning is leveraged to escape from infeasible initial value's local minima. Then spatial-temporal optimization is employed to generate a safe, smooth and dynamically feasible trajectory. During optimization, the trajectory is discretized by fixed time steps. Penalty is imposed on the signed distance between agents to realize collision avoidance, and differential flatness cooperated with limitation on front steer angle satisfies the non-holonomic constraints. With trajectories broadcast to the wireless network, agents are able to check and prevent potential collisions. We validate the robustness of our system in simulation and real-world experiments. Code will be released as open-source packages.

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AAAI Conference 2023 Conference Paper

EMEF: Ensemble Multi-Exposure Image Fusion

Renshuai Liu
Chengyang Li
Haitao Cao
Yinglin Zheng
Ming Zeng
Xuan Cheng

Although remarkable progress has been made in recent years, current multi-exposure image fusion (MEF) research is still bounded by the lack of real ground truth, objective evaluation function, and robust fusion strategy. In this paper, we study the MEF problem from a new perspective. We don’t utilize any synthesized ground truth, design any loss function, or develop any fusion strategy. Our proposed method EMEF takes advantage of the wisdom of multiple imperfect MEF contributors including both conventional and deep learning-based methods. Specifically, EMEF consists of two main stages: pre-train an imitator network and tune the imitator in the runtime. In the first stage, we make a unified network imitate different MEF targets in a style modulation way. In the second stage, we tune the imitator network by optimizing the style code, in order to find an optimal fusion result for each input pair. In the experiment, we construct EMEF from four state-of-the-art MEF methods and then make comparisons with the individuals and several other competitive methods on the latest released MEF benchmark dataset. The promising experimental results demonstrate that our ensemble framework can “get the best of all worlds”. The code is available at https://github.com/medalwill/EMEF.

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AAAI Conference 2023 Conference Paper

Weakly Supervised 3D Segmentation via Receptive-Driven Pseudo Label Consistency and Structural Consistency

Yuxiang Lan
Yachao Zhang
Yanyun Qu
Cong Wang
Chengyang Li
Jia Cai
Yuan Xie
Zongze Wu

As manual point-wise label is time and labor-intensive for fully supervised large-scale point cloud semantic segmentation, weakly supervised method is increasingly active. However, existing methods fail to generate high-quality pseudo labels effectively, leading to unsatisfactory results. In this paper, we propose a weakly supervised point cloud semantic segmentation framework via receptive-driven pseudo label consistency and structural consistency to mine potential knowledge. Specifically, we propose three consistency contrains: pseudo label consistency among different scales, semantic structure consistency between intra-class features and class-level relation structure consistency between pair-wise categories. Three consistency constraints are jointly used to effectively prepares and utilizes pseudo labels simultaneously for stable training. Finally, extensive experimental results on three challenging datasets demonstrate that our method significantly outperforms state-of-the-art weakly supervised methods and even achieves comparable performance to the fully supervised methods.

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IROS Conference 2022 Conference Paper

Adaptive Environment Modeling Based Reinforcement Learning for Collision Avoidance in Complex Scenes

Shuaijun Wang
Rui Gao 0008
Ruihua Han
Shengduo Chen
Chengyang Li
Qi Hao 0003

The major challenges of collision avoidance for robot navigation in crowded scenes lie in accurate environment modeling, fast perceptions, and trustworthy motion planning policies. This paper presents a novel adaptive environment model based collision avoidance reinforcement learning (i. e. , AEMCARL) framework for an unmanned robot to achieve collision-free motions in challenging navigation scenarios. The novelty of this work is threefold: (1) developing a hierarchical network of gated-recurrent-unit (GRU) for environment modeling; (2) developing an adaptive perception mechanism with an attention module; (3) developing an adaptive reward function for the reinforcement learning (RL) framework to jointly train the environment model, perception function and motion planning policy. The proposed method is tested with the Gym-Gazebo simulator and a group of robots (Husky and Turtlebot) under various crowded scenes. Both simulation and experimental results have demonstrated the superior performance of the proposed method over baseline methods.

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