@article{zuo2020multimodalls,
title = {Multimodal localization: Stereo over LiDAR map},
author = {Xingxing Zuo and Wenlong Ye and Yulin Yang and Renjie Zheng and Teresa Vidal-Calleja and Guoquan Huang and Yong Liu},
year = 2020,
journal = {Journal of Field Robotics},
volume = 37,
pages = {1003--1026},
doi = {https://doi.org/10.1002/rob.21936},
abstract = {In this paper, we present a real‐time high‐precision visual localization system for an autonomous vehicle which employs only low‐cost stereo cameras to localize the vehicle with a priori map built using a more expensive 3D LiDAR sensor. To this end, we construct two different visual maps: a sparse feature visual map for visual odometry (VO) based motion tracking, and a semidense visual map for registration with the prior LiDAR map. To register two point clouds sourced from different modalities (i.e., cameras and LiDAR), we leverage probabilistic weighted normal distributions transformation (ProW‐NDT), by particularly taking into account the uncertainty of source point clouds. The registration results are then fused via pose graph optimization to correct the VO drift. Moreover, surfels extracted from the prior LiDAR map are used to refine the sparse 3D visual features that will further improve VO‐based motion estimation. The proposed system has been tested extensively in both simulated and real‐world experiments, showing that robust, high‐precision, real‐time localization can be achieved.}
}

@article{liao2017graphra,
title = {Graph Regularized Auto-Encoders for Image Representation},
author = {Yiyi Liao and Yue Wang and Yong Liu},
year = 2017,
journal = {IEEE Transactions on Image Processing},
volume = 26,
pages = {2839--2852},
doi = {https://doi.org/10.1109/TIP.2016.2605010},
abstract = {Image representation has been intensively explored in the domain of computer vision for its significant influence on the relative tasks such as image clustering and classification. It is valuable to learn a low-dimensional representation of an image which preserves its inherent information from the original image space. At the perspective of manifold learning, this is implemented with the local invariant idea to capture the intrinsic low-dimensional manifold embedded in the high-dimensional input space. Inspired by the recent successes of deep architectures, we propose a local invariant deep nonlinear mapping algorithm, called graph regularized auto-encoder (GAE). With the graph regularization, the proposed method preserves the local connectivity from the original image space to the representation space, while the stacked auto-encoders provide explicit encoding model for fast inference and powerful expressive capacity for complex modeling. Theoretical analysis shows that the graph regularizer penalizes the weighted Frobenius norm of the Jacobian matrix of the encoder mapping, where the weight matrix captures the local property in the input space. Furthermore, the underlying effects on the hidden representation space are revealed, providing insightful explanation to the advantage of the proposed method. Finally, the experimental results on both clustering and classification tasks demonstrate the effectiveness of our GAE as well as the correctness of the proposed theoretical analysis, and it also suggests that GAE is a superior solution to the current deep representation learning techniques comparing with variant auto-encoders and existing local invariant methods.}
}

@article{wang2017scalablelf,
title = {Scalable Learning Framework for Traversable Region Detection Fusing With Appearance and Geometrical Information},
author = {Yue Wang and Yong Liu and Yiyi Liao and Rong Xiong},
year = 2017,
journal = {IEEE Transactions on Intelligent Transportation Systems},
volume = 18,
pages = {3267--3281},
doi = {https://doi.org/10.1109/TITS.2017.2682218},
abstract = {In this paper, we present an online learning framework for traversable region detection fusing both appearance and geometry information. Our framework proposes an appearance classifier supervised by the sparse geometric clues to capture the variation in online data, yielding dense detection result in real time. It provides superior detection performance using appearance information with weak geometric prior and can be further improved with more geometry from external sensors. The learning process is divided into three steps: First, we construct features from the super-pixel level, which reduces the computational cost compared with the pixel level processing. Then we classify the multi-scale super-pixels to vote the label of each pixel. Second, we use weighted extreme learning machine as our classifier to deal with the imbalanced data distribution since the weak geometric prior only initializes the labels in a small region. Finally, we employ the online learning process so that our framework can be adaptive to the changing scenes. Experimental results on three different styles of image sequences, i.e., shadow road, rain sequence, and variational sequence, demonstrate the adaptability, stability, and parameter insensitivity of our weak geometry motivated method. We further demonstrate the performance of learning framework on additional five challenging data sets captured by Kinect V2 and stereo camera, validating the method’s effectiveness and efficiency.}
}

@article{liu2016stereovo,
title = {Stereo Visual-Inertial Odometry With Multiple Kalman Filters Ensemble},
author = {Yong Liu and Rong Xiong and Yue Wang and Hong Huang and Xiaojia Xie and Xiaofeng Liu and Gaoming Zhang},
year = 2016,
journal = {IEEE Transactions on Industrial Electronics},
volume = 63,
pages = {6205--6216},
doi = {https://doi.org/10.1109/TIE.2016.2573765},
abstract = {In this paper, we present a stereo visual-inertial odometry algorithm assembled with three separated Kalman filters, i.e., attitude filter, orientation filter, and position filter. Our algorithm carries out the orientation and position estimation with three filters working on different fusion intervals, which can provide more robustness even when the visual odometry estimation fails. In our orientation estimation, we propose an improved indirect Kalman filter, which uses the orientation error space represented by unit quaternion as the state of the filter. The performance of the algorithm is demonstrated through extensive experimental results, including the benchmark KITTI datasets and some challenging datasets captured in a rough terrain campus.}
}

@article{liu2015featuresb,
title = {Feature Selection Based on Dependency Margin},
author = {Yong Liu and Feng Tang and Zhiyong Zeng},
year = 2015,
journal = {IEEE Transactions on Cybernetics},
volume = 45,
pages = {1209--1221},
doi = {https://doi.org/10.1109/TCYB.2014.2347372},
abstract = {Feature selection tries to find a subset of feature from a larger feature pool and the selected subset can provide the same or even better performance compared with using the whole set. Feature selection is usually a critical preprocessing step for many machine-learning applications such as clustering and classification. In this paper, we focus on feature selection for supervised classification which targets at finding features that can best predict class labels. Traditional greedy search algorithms incrementally find features based on the relevance of candidate features and the class label. However, this may lead to suboptimal results when there are redundant features that may interfere with the selection. To solve this problem, we propose a subset selection algorithm that considers both the selected and remaining features' relevances with the label. The intuition is that features, which do not have better alternatives from the feature set, should be selected first. We formulate the selection problem as maximizing the dependency margin which is measured by the difference between the selected feature set performance and the remaining feature set performance. Extensive experiments on various data sets show the superiority of the proposed approach against traditional algorithms.}
}

@article{lang2023lic,
title = {Coco-LIC: Continuous-Time Tightly-Coupled LiDAR-Inertial-Camera Odometry using Non-Uniform B-spline},
author = {Xiaolei Lang and Chao Chen and Kai Tang and Yukai Ma and Jiajun Lv and Yong Liu and Xingxing Zuo},
year = 2023,
journal = {IEEE Robotics and Automation Letters},
volume = 8,
pages = {7074-7081},
doi = {10.1109/LRA.2023.3315542},
abstract = {In this paper, we propose an effcient continuous-time LiDAR-Inertial-Camera Odometry, utilizing non-uniform B-splines to tightly couple measurements from the LiDAR, IMU, and camera. In contrast to uniform B-spline-based continuous-time methods, our non-uniform B-spline approach offers signifcant advantages in terms of achieving real-time effciency and high accuracy. This is accomplished by dynamically and adaptively placing control points, taking into account the varying dynamics of the motion. To enable effcient fusion of heterogeneous LiDAR-Inertial-Camera data within a short sliding-window optimization, we assign depth to visual pixels using corresponding map points from a global LiDAR map, and formulate frame-to-map reprojection factors for the associated pixels in the current image frame. This way circumvents the necessity for depth optimization of visual pixels, which typically entails a lengthy sliding window with numerous control points for continuous-time trajectory estimation. We conduct dedicated experiments on real-world datasets to demonstrate the advantage and effcacy of adopting non-uniform continuous-time trajectory representation. Our LiDAR-Inertial-Camera odometry system is also extensively evaluated on both challenging scenarios with sensor degenerations and large-scale scenarios, and has shown comparable or higher accuracy than the state-of-the-art methods. The codebase of this paper will also be open-sourced at https://github.com/APRIL-ZJU/Coco-LIC .}
}

@article{jiang2023ssp,
title = {Single-Shot Pruning and Quantization for Hardware-Friendly Neural Network Acceleration},
author = {Bofeng Jiang and Jun Chen and Yong Liu},
year = 2023,
journal = {Engineering Applications of Artificial Intelligence},
volume = 126,
doi = {10.1016/j.engappai.2023.106816},
abstract = {Applying CNN on embedded systems is challenging due to model size limitations. Pruning and quantization can help, but are time-consuming to apply separately. Our Single-Shot Pruning and Quantization strategy addresses these issues by quantizing and pruning in a single process. We evaluated our method on CIFAR-10 and CIFAR-100 datasets for image classification. Our model is 69.4% smaller with little accuracy loss, and runs 6-8 times faster on NVIDIA Xavier NX hardware.}
}

@article{liu2023edg,
title = {Expert Demonstrations Guide Reward Decomposition for Multi-Agent Cooperation},
author = {Weiwei Liu and Wei Jing and Shanqi Liu and Yudi Ruan and Kexin Zhang and Jian Yang and Yong Liu},
year = 2023,
journal = {Neural Computing and Applications},
volume = 35,
pages = {19847-19863},
doi = {10.1007/s00521-023-08785-6},
abstract = {Humans are able to achieve good teamwork through collaboration, since the contributions of the actions from human team members are properly understood by each individual. Therefore, reasonable credit assignment is crucial for multi-agent cooperation. Although existing work uses value decomposition algorithms to mitigate the credit assignment problem, since they decompose the global value function at multi-agents' local value function level, the overall evaluation of the value function can easily lead to approximation errors. Moreover, such strategies are vulnerable to sparse reward scenarios. In this paper, we propose to use expert demonstrations to guide the team reward decomposition at each time step, rather than value decomposition. The proposed method computes the reward ratio of each agent according to the similarity between the state-action pair of the agent and the expert demonstrations. In addition, under this setting, each agent can independently train its value function and evaluate its behavior, which makes the algorithm highly robust to team rewards. Moreover, the proposed method constrains the policy to collect data with similar distribution to the expert data during the exploration, which makes policy update more robust. We conduct extensive experiments to validate our proposed method in various MARL environments, the results show that our algorithm outperforms the state-of-the-art algorithms in most scenarios; our method is robust to various reward functions; and the trajectories by our policy is closer to that of the expert policy.}
}