Author name cluster

Wu Li

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7 papers

2 author rows

IROS Conference 2025 Conference Paper

MSPA-LIO: LiDAR-Inertial Odometry with Multi-Scale Plane Adjustment

Su Yan
Shuying Zhao
Yunzhou Zhang
Hengwang Ding
Wu Li
Sizhan Wang
Song Wu

Most current LiDAR-based odometry methods use point-to-local plane registration to constrain poses, ignoring the explicit plane structure in the environment. Due to noise interference and uneven distribution of point cloud, local planes are prone to tilt, resulting in registration errors. Therefore, we propose MSPA-LIO, a LiDAR-Inertial odometry with multi-scale plane adjustment, which uses geometric constraints and plane adjustment at both local voxel plane scale and large plane scale to improve odometry accuracy and enhance map consistency. In order to make full use of the planar structure in the environment, we propose an explicit large plane extraction method based on the voxel-based. We use large planes to correct the direction of the associated voxel planes, thereby overcoming the misregistration problem caused by local plane tilt. To further improve the odometry accuracy, we perform plane adjustments at the voxel plane scale and the large plane scale to make the pose and map more consistent. Experiments conducted on the VECtor Dataset and the Newer College Dataset demonstrate that our proposed algorithm outperforms four state-of-the-art algorithms.

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ICRA Conference 2024 Conference Paper

CTA-LO: Accurate and Robust LiDAR Odometry Using Continuous-Time Adaptive Estimation

Yuezhang Lv
Yunzhou Zhang
Xiaoyu Zhao
Wu Li
Jian Ning
Yang Jin

Accurate and robust LiDAR odometry is a crucial technology for robot localization. However, motion distortion and ranging error make it a bottleneck. Most existing methods are limited in accuracy and robustness because they simply compensate for motion distortion by constant velocity motion assumption without accurate model of ranging error. In this paper, we propose a high-precision and robust LiDAR odometry (LO), which utilizes continuous-time estimation to remove LiDAR distortion and builds the spot uncertainty model to quantify the ranging error. Generally, the number of variables in continuous-time estimation is several times higher than that in discrete-time ones, leading to insufficient constraints on the LiDAR odometry. To solve this problem, we propose a marginalization method to retain prior scans’ constraints by exploiting the local support property of the B-spline. To further improve the odometry accuracy, we propose a residual adaptive weighting method and a probabilistic point cloud map based on the spot uncertainty model of LiDAR points. The experimental results show that our method outperforms state-of-the-art LiDAR odometry in accuracy and robustness.

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IROS Conference 2024 Conference Paper

ESO-SLAM: Tightly-Coupled and Simultaneous Estimation of Self and Multi-Object Pose via Sensor Fusion

Wu Li
Yunzhou Zhang
Yuezhang Lv
Tingting Wang
Sizhan Wang
Guiyuan Wang

Simultaneous Localization and Mapping (SLAM) is widely used in applications such as robotics and autonomous driving, with methods involving multi-sensor fusion demonstrating excellent performance. However, they simply reject dynamic features and ignore the mutual benefits of self and dynamic objects, which greatly limits their application in actual high-dynamic scenes. To address this issue, we propose ESO-SLAM, a tightly-coupled system for simultaneous self and multi-object pose estimation achieved through sensor fusion. This system employs a multi-probability fusion tracker based on filter to establish more robust object-level data association. Building upon this, we introduce a method that combines 3D Kalman filter velocity priors and camera optical flow decoupling for dynamic point cloud removal, aiming at improving the accuracy of self-pose estimation in odometry. Finally, we jointly refine the poses of the robot and objects using multiple constraint factors within our proposed framework. Experimental results on the KITTI raw dataset demonstrate that our approach achieves better pose accuracy for both self and tracked objects compared to baseline and state-of-the-art techniques. Furthermore, the proposed method exhibits feasibility in real-time performance to ensure its practical application value.

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YNIMG Journal 2016 Journal Article

Joint analysis of spikes and local field potentials using copula

Meng Hu
Mingyao Li
Wu Li
Hualou Liang

Recent technological advances, which allow for simultaneous recording of spikes and local field potentials (LFPs) at multiple sites in a given cortical area or across different areas, have greatly increased our understanding of signal processing in brain circuits. Joint analysis of simultaneously collected spike and LFP signals is an important step to explicate how the brain orchestrates information processing. In this contribution, we present a novel statistical framework based on Gaussian copula to jointly model spikes and LFP. In our approach, we use copula to link separate, marginal regression models to construct a joint regression model, in which the binary-valued spike train data are modeled using generalized linear model (GLM) and the continuous-valued LFP data are modeled using linear regression. Model parameters can be efficiently estimated via maximum-likelihood. In particular, we show that our model offers a means to statistically detect directional influence between spikes and LFP, akin to Granger causality measure, and that we are able to assess its statistical significance by conducting a Wald test. Through extensive simulations, we also show that our method is able to reliably recover the true model used to generate the data. To demonstrate the effectiveness of our approach in real setting, we further apply the method to a mixed neural dataset, consisting of spikes and LFP simultaneously recorded from the visual cortex of a monkey performing a contour detection task.

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YNIMG Journal 2016 Journal Article

Predicting perceptual learning from higher-order cortical processing

Fang Wang
Jing Huang
Yaping Lv
Xiaoli Ma
Bin Yang
Encong Wang
Boqi Du
Wu Li

Visual perceptual learning has been shown to be highly specific to the retinotopic location and attributes of the trained stimulus. Recent psychophysical studies suggest that these specificities, which have been associated with early retinotopic visual cortex, may in fact not be inherent in perceptual learning and could be related to higher-order brain functions. Here we provide direct electrophysiological evidence in support of this proposition. In a series of event-related potential (ERP) experiments, we recorded high-density electroencephalography (EEG) from human adults over the course of learning in a texture discrimination task (TDT). The results consistently showed that the earliest C1 component (68–84ms), known to reflect V1 activity driven by feedforward inputs, was not modulated by learning regardless of whether the behavioral improvement is location specific or not. In contrast, two later posterior ERP components (posterior P1 and P160–350) over the occipital cortex and one anterior ERP component (anterior P160–350) over the prefrontal cortex were progressively modified day by day. Moreover, the change of the anterior component was closely correlated with improved behavioral performance on a daily basis. Consistent with recent psychophysical and imaging observations, our results indicate that perceptual learning can mainly involve changes in higher-level visual cortex as well as in the neural networks responsible for cognitive functions such as attention and decision making.

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YNIMG Journal 2011 Journal Article

A weighted-RV method to detect fine-scale functional connectivity during resting state

Hui Zhang
Xiaopeng Zhang
Yingshi Sun
Jiangang Liu
Wu Li
Jie Tian

During the resting state, in the absence of external stimuli or goal-directed mental tasks, some functionally related discrete regions of the brain show complex low-frequency fluctuations in the blood oxygenation level dependent signal. Here we developed a novel ROI-based multivariate statistical framework to obtain the fine-grained patterns of functionally specialized brain networks in the resting state. Under this framework, the weighted-RV method is proposed and used to detect the spatial fine-scale patterns of functional connectivity. This approach overcomes several major problems of the traditional resting-state data analysis methods such as Pearson correlation and linear regression analysis. By using simulation and real fMRI experiment, we have found that the weighted-RV method is shown to be more sensitive in detecting the fine-scale based low-frequency connectivity even at a very low functional contrast-to-noise ratio (CNR), and this method can achieve much better performance in mapping the fine-grained patterns of functionally specialized brain networks compared to the traditional methods.

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YNIMG Journal 2004 Journal Article

Robust unsupervised segmentation of infarct lesion from diffusion tensor MR images using multiscale statistical classification and partial volume voxel reclassification

Wu Li
Jie Tian
Enzhong Li
Jianping Dai

Manual region tracing method for segmentation of infarction lesions in images from diffusion tensor magnetic resonance imaging (DT-MRI) is usually used in clinical works, but it is time consuming. A new unsupervised method has been developed, which is a multistage procedure, involving image preprocessing, calculation of tensor field and measurement of diffusion anisotropy, segmentation of infarction volume based on adaptive multiscale statistical classification (MSSC), and partial volume voxel reclassification (PVVR). The method accounts for random noise, intensity overlapping, partial volume effect (PVE), and intensity shading artifacts, which always appear in DT-MR images. The proposed method was applied to 20 patients with clinically diagnosed brain infarction by DT-MRI scans. The accuracy and reproducibility in terms of identifying the infarction lesion have been confirmed by clinical experts. This automatic segmentation method is promising not only in detecting the location and the size of infarction lesion in stroke patient but also in quantitatively analyzing diffusion anisotropy of lesion to guide clinical diagnoses and therapy.

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