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Ming Liu

Publications and source records attributed to Ming Liu.

At least 487 records · Page 27Linked to original sources
arXiv2017

LiDAR and Inertial Fusion for Pose Estimation by Non-linear Optimization

Pose estimation purely based on 3D point-cloud could suffer from degradation, e.g. scan blocks or scans in repetitive environments. To deal with this problem, we propose an approach for fusing 3D spinning LiDAR and IMU to estimate the ego-motion of the sensor body. The main idea of our work is to optimize the poses and states of two kinds of sensors with non-linear optimization methods. On the one hand, a bunch of IMU measurements are considered as a relative constraint using pre-integration and the state errors can be minimized with the help of laser pose estimation and non-linear optimization algorithms; on the other hand, the optimized IMU pose outputs can provide a better initial for the subsequent point-cloud matching. The method is evaluated under both simulation and real tests with comparison to the state-of-the-art. The results show that the proposed method can provide better pose estimation performance even in the degradation cases.

cs.RO↗
arXiv2017

Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning

In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach. The work is based on the deep deterministic policy gradient (DDPG) algorithm, proven to be very successful in simple simulation environments. The algorithm works in an end-to-end fashion in order to control the continuous position of the flippers. This end-to-end approach makes it easy to apply the controller to a wide array of circumstances, but the huge flexibility comes to the cost of an increased difficulty of solution. The complexity of the task is enlarged even more by the fact that real multi-terrain robots move in partially observable environments. Notwithstanding these complications, being able to smoothly control a multi-terrain robot can produce huge benefits in impaired people daily lives or in search and rescue situations.

cs.RO↗
arXiv2017

Characterization of a RS-LiDAR for 3D Perception

High precision 3D LiDARs are still expensive and hard to acquire. This paper presents the characteristics of RS-LiDAR, a model of low-cost LiDAR with sufficient supplies, in comparison with VLP-16. The paper also provides a set of evaluations to analyze the characterizations and performances of LiDARs sensors. This work analyzes multiple properties, such as drift effects, distance effects, color effects and sensor orientation effects, in the context of 3D perception. By comparing with Velodyne LiDAR, we found RS-LiDAR as a cheaper and acquirable substitute of VLP-16 with similar efficiency.

cs.RO↗
arXiv2017

Plugo: a VLC Systematic Perspective of Large-scale Indoor Localization

Indoor localization based on Visible Light Communication (VLC) has been in favor with both the academia and industry for years. In this paper, we present a prototyping photodiode-based VLC system towards large-scale localization. Specially, we give in-depth analysis of the design constraints and considerations for large-scale indoor localization research. After that we identify the key enablers for such systems: 1) distributed architecture, 2) one-way communication, and 3) random multiple access. Accordingly, we propose Plugo -- a photodiode-based VLC system conforming to the aforementioned criteria. We present a compact design of the VLC-compatible LED bulbs featuring plug-and-go use-cases. The basic framed slotted Additive Links On-line Hawaii Area (ALOHA) is exploited to achieve random multiple access over the shared optical medium. We show its effectiveness in beacon broadcasting by experiments, and further demonstrate its scalability to large-scale scenarios through simulations. Finally, preliminary localization experiments are conducted using fingerprinting-based methods in a customized testbed, achieving an average accuracy of 0.14 m along with a 90-percentile accuracy of 0.33 m.

cs.NI↗
arXiv2017

Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation

We present a learning-based mapless motion planner by taking the sparse 10-dimensional range findings and the target position with respect to the mobile robot coordinate frame as input and the continuous steering commands as output. Traditional motion planners for mobile ground robots with a laser range sensor mostly depend on the obstacle map of the navigation environment where both the highly precise laser sensor and the obstacle map building work of the environment are indispensable. We show that, through an asynchronous deep reinforcement learning method, a mapless motion planner can be trained end-to-end without any manually designed features and prior demonstrations. The trained planner can be directly applied in unseen virtual and real environments. The experiments show that the proposed mapless motion planner can navigate the nonholonomic mobile robot to the desired targets without colliding with any obstacles.

cs.RO↗
arXiv2017

PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI

Semantic segmentation of functional magnetic resonance imaging (fMRI) makes great sense for pathology diagnosis and decision system of medical robots. The multi-channel fMRI provides more information of the pathological features. But the increased amount of data causes complexity in feature detections. This paper proposes a principal component analysis (PCA)-aided fully convolutional network to particularly deal with multi-channel fMRI. We transfer the learned weights of contemporary classification networks to the segmentation task by fine-tuning. The results of the convolutional network are compared with various methods e.g. k-NN. A new labeling strategy is proposed to solve the semantic segmentation problem with unclear boundaries. Even with a small-sized training dataset, the test results demonstrate that our model outperforms other pathological feature detection methods. Besides, its forward inference only takes 90 milliseconds for a single set of fMRI data. To our knowledge, this is the first time to realize pixel-wise labeling of multi-channel magnetic resonance image using FCN.

cs.CV↗
arXiv2017

Experiments on bright field and dark field high energy electron imaging with thick target material

Using a high energy electron beam for the imaging of high density matter with both high spatial-temporal and areal density resolution under extreme states of temperature and pressure is one of the critical challenges in high energy density physics . When a charged particle beam passes through an opaque target, the beam will be scattered with a distribution that depends on the thickness of the material. By collecting the scattered beam either near or off axis, so-called bright field or dark field images can be obtained. Here we report on an electron radiography experiment using 45 MeV electrons from an S-band photo-injector, where scattered electrons, after interacting with a sample, are collected and imaged by a quadrupole imaging system. We achieved a few micrometers (about 4 micrometers) spatial resolution and about 10 micrometers thickness resolution for a silicon target of 300-600 micron thickness. With addition of dark field images that are captured by selecting electrons with large scattering angle, we show that more useful information in determining external details such as outlines, boundaries and defects can be obtained.

physics.acc-ph↗
arXiv2017

Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention

Fusion of heterogeneous extroceptive sensors is the most effient and effective way to representing the environment precisely, as it overcomes various defects of each homogeneous sensor. The rigid transformation (aka. extrinsic parameters) of heterogeneous sensory systems should be available before precisely fusing the multisensor information. Researchers have proposed several approaches to estimating the extrinsic parameters. These approaches require either auxiliary objects, like chessboards, or extra help from human to select correspondences. In this paper, we proposed a novel extrinsic calibration approach for the extrinsic calibration of range and image sensors. As far as we know, it is the first automatic approach with no requirement of auxiliary objects or any human interventions. First, we estimate the initial extrinsic parameters from the individual motion of the range finder and the camera. Then we extract lines in the image and point-cloud pairs, to refine the line feature associations by the initial extrinsic parameters. At the end, we discussed the degenerate case which may lead to the algorithm failure and validate our approach by simulation. The results indicate high-precision extrinsic calibration results against the ground-truth.

cs.CV↗
arXiv2017

A review for compact model of graphene field-effect transistors

Graphene has attracted enormous interests due to its unique physical, mechanical, and electrical properties. Specially, graphene-based field-effect transistors (FETs) have evolved rapidly and are now considered as an option for conventional silicon devices. As a critical step in the design cycle of modern IC products, compact model refers to the development of models for integrated semiconductor devices for use in circuit simulations. The purpose of this review is to provide a theoretical description of current compact model of graphene field-effect transistors. Special attention is devoted to the charge sheet model, drift-diffusion model, Boltzmann equation, density of states (DOS), and surface-potential-based compact model. Finally, an outlook of this field is briefly discussed.

cond-mat.mes-hall↗
arXiv2016

Towards Cognitive Exploration through Deep Reinforcement Learning for Mobile Robots

Exploration in an unknown environment is the core functionality for mobile robots. Learning-based exploration methods, including convolutional neural networks, provide excellent strategies without human-designed logic for the feature extraction. But the conventional supervised learning algorithms cost lots of efforts on the labeling work of datasets inevitably. Scenes not included in the training set are mostly unrecognized either. We propose a deep reinforcement learning method for the exploration of mobile robots in an indoor environment with the depth information from an RGB-D sensor only. Based on the Deep Q-Network framework, the raw depth image is taken as the only input to estimate the Q values corresponding to all moving commands. The training of the network weights is end-to-end. In arbitrarily constructed simulation environments, we show that the robot can be quickly adapted to unfamiliar scenes without any man-made labeling. Besides, through analysis of receptive fields of feature representations, deep reinforcement learning motivates the convolutional networks to estimate the traversability of the scenes. The test results are compared with the exploration strategies separately based on deep learning or reinforcement learning. Even trained only in the simulated environment, experimental results in real-world environment demonstrate that the cognitive ability of robot controller is dramatically improved compared with the supervised method. We believe it is the first time that raw sensor information is used to build cognitive exploration strategy for mobile robots through end-to-end deep reinforcement learning.

cs.RO↗
arXiv2016

Rota-Baxter operators on Witt and Virasoro algebras

The homogeneous Rota-Baxter operators on the Witt and Virasoro algebras are classified. As applications, the induced solutions of the classical Yang-Baxter equation and the induced pre-Lie and PostLie algebra structures are obtained.

math-ph↗
arXiv2015

Beam optical design for high energy electron radiography experiment study based on THU LINAC

A special beam line for high energy electron radiography is designed, including achromat and imaging systems. The requirement of the angle and position correction on the target from imaging system can be approximately realized by fine tuning the quadrupoles used in the achromat. The imaging system is designed by fully considering the limitation from the laboratory and beam diagnostics devices space. Two kinds of imaging system are designed and both show a good performance of imaging by beam trajectory simulation. The details of the beam optical requirement and optimization design are presented here. The beam line is designed and prepared to install in Tsinghua university linear electron accelerator laboratory for further precise electron radiography experiment study.

physics.acc-ph↗
arXiv2015

Control of magnetic relaxation by electric-field-induced ferroelectric phase transition and inhomogeneous domain switching

Electric-field modulation of magnetism in strain-mediated multiferroic heterostructures is considered a promising scheme for enabling memory and magnetic microwave devices with ultralow power consumption. However, it is not well understood how electric-field-induced strain influences magnetic relaxation, an important physical process for device applications. Here we investigate resonant magnetization dynamics in ferromagnet/ferrolectric multiferroic heterostructures, FeGaB/PMN-PT and NiFe/PMN-PT, in two distinct strain states provided by electric-field-induced ferroelectric phase transition. The strain not only modifies magnetic anisotropy but also magnetic relaxation. In FeGaB/PMN-PT, we observe a nearly two-fold change in intrinsic Gilbert damping by electric field, which is attributed to strain-induced tuning of spin-orbit coupling. By contrast, a small but measurable change in extrinsic linewidth broadening is attributed to inhomogeneous ferroelastic domain switching during the phase transition of the PMN-PT substrate.

cond-mat.mtrl-sci↗
arXiv2014

Pre-Town Meeting on Spin Physics at an Electron-Ion Collider

A polarized $ep/eA$ collider (Electron--Ion Collider, or EIC), with polarized proton and light-ion beams and unpolarized heavy-ion beams with a variable center--of--mass energy $\sqrt{s} \sim 20$ to $\sim100$~GeV (upgradable to $\sim 150$ GeV) and a luminosity up to $\sim 10^{34} \, \textrm{cm}^{-2} \textrm{s}^{-1}$, would be uniquely suited to address several outstanding questions of Quantum Chromodynamics, and thereby lead to new qualitative and quantitative information on the microscopic structure of hadrons and nuclei. During this meeting at Jefferson Lab we addressed recent theoretical and experimental developments in the spin and the three--dimensional structure of the nucleon (sea quark and gluon spatial distributions, orbital motion, polarization, and their correlations). This mini--review contains a short update on progress in these areas since the EIC White paper~\cite{Accardi:2012qut}.

hep-ph↗
arXiv2014

Reversal-mechanism of in-plane current induced perpendicular switching in a single ferromagnetic layer

We propose a magnetization reversal model to explain the perpendicular switching of a single ferromagnetic layer induced by an in-plane current. Contrary to previously proposed reversal mechanisms that such magnetic switching is directly from the Rashba or spin Hall effects, we suggest that this type of switching arises from the current-induced chirality dependent domain wall motion. By measuring the field dependent switching behaviors, we show that such switching can also be achieved between any two multidomain states, and all of these switching behaviors can be well explained by this model. This model indicates that the spin Hall angle in such structures may be overestimated and also predicts similar switching behaviors in other ferromagnetic structures with chiral domain walls or skyrmions.

cond-mat.mes-hall↗
arXiv2014

Observation of Piezoelectricity in Monolayer Molybdenum Disulfide

Piezoelectricity offers precise and robust conversion between electricity and mechanical force. Here we report the first experimental evidence of piezoelectricity in a single layer of molybdenum disulfide (MoS2) crystal as a result of inversion symmetry breaking of the atomic structure, with measured piezoelectric coefficient e11 = 2.9e-10 C/m. Through the angular dependence of electro-mechanical coupling, we uniquely determined the two-dimensional (2D) crystal orientation. We observed that only MoS2 membranes with odd number of layers exhibited piezoelectricity, in sharp contrast to the conventional materials. The piezoelectricity discovered in single molecular membrane promises scaling down of nano-electro-mechanical systems (NEMS) to single atomic unit cell - the ultimate material limit.

cond-mat.mtrl-sci↗