arXiv subjects

Xiaoping Hong

Publications and source records attributed to Xiaoping Hong.

13 recordsLinked to original sources

arXiv2023

Coarse-to-fine Hybrid 3D Mapping System with Co-calibrated Omnidirectional Camera and Non-repetitive LiDAR

This paper presents a novel 3D mapping robot with an omnidirectional field-of-view (FoV) sensor suite composed of a non-repetitive LiDAR and an omnidirectional camera. Thanks to the non-repetitive scanning nature of the LiDAR, an automatic targetless co-calibration method is proposed to simultaneously calibrate the intrinsic parameters for the omnidirectional camera and the extrinsic parameters for the camera and LiDAR, which is crucial for the required step in bringing color and texture information to the point clouds in surveying and mapping tasks. Comparisons and analyses are made to target-based intrinsic calibration and mutual information (MI)-based extrinsic calibration, respectively. With this co-calibrated sensor suite, the hybrid mapping robot integrates both the odometry-based mapping mode and stationary mapping mode. Meanwhile, we proposed a new workflow to achieve coarse-to-fine mapping, including efficient and coarse mapping in a global environment with odometry-based mapping mode; planning for viewpoints in the region-of-interest (ROI) based on the coarse map (relies on the previous work); navigating to each viewpoint and performing finer and more precise stationary scanning and mapping of the ROI. The fine map is stitched with the global coarse map, which provides a more efficient and precise result than the conventional stationary approaches and the emerging odometry-based approaches, respectively.

cs.RO↗

arXiv2023

ImMesh: An Immediate LiDAR Localization and Meshing Framework

In this paper, we propose a novel LiDAR(-inertial) odometry and mapping framework to achieve the goal of simultaneous localization and meshing in real-time. This proposed framework termed ImMesh comprises four tightly-coupled modules: receiver, localization, meshing, and broadcaster. The localization module utilizes the prepossessed sensor data from the receiver, estimates the sensor pose online by registering LiDAR scans to maps, and dynamically grows the map. Then, our meshing module takes the registered LiDAR scan for incrementally reconstructing the triangle mesh on the fly. Finally, the real-time odometry, map, and mesh are published via our broadcaster. The key contribution of this work is the meshing module, which represents a scene by an efficient hierarchical voxels structure, performs fast finding of voxels observed by new scans, and reconstructs triangle facets in each voxel in an incremental manner. This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3D points to a 2D local plane contained in the voxel, and then executes the meshing operation with pull, commit and push steps for incremental reconstruction of triangle facets. To the best of our knowledge, this is the first work in literature that can reconstruct online the triangle mesh of large-scale scenes, just relying on a standard CPU without GPU acceleration. To share our findings and make contributions to the community, we make our code publicly available on our GitHub: https://github.com/hku-mars/ImMesh.

cs.RO↗

arXiv2022

STD: Stable Triangle Descriptor for 3D place recognition

In this work, we present a novel global descriptor termed stable triangle descriptor (STD) for 3D place recognition. For a triangle, its shape is uniquely determined by the length of the sides or included angles. Moreover, the shape of triangles is completely invariant to rigid transformations. Based on this property, we first design an algorithm to efficiently extract local key points from the 3D point cloud and encode these key points into triangular descriptors. Then, place recognition is achieved by matching the side lengths (and some other information) of the descriptors between point clouds. The point correspondence obtained from the descriptor matching pair can be further used in geometric verification, which greatly improves the accuracy of place recognition. In our experiments, we extensively compare our proposed system against other state-of-the-art systems (i.e., M2DP, Scan Context) on public datasets (i.e., KITTI, NCLT, and Complex-Urban) and our self-collected dataset (with a non-repetitive scanning solid-state LiDAR). All the quantitative results show that STD has stronger adaptability and a great improvement in precision over its counterparts. To share our findings and make contributions to the community, we open source our code on our GitHub: https://github.com/hku-mars/STD.

cs.CV↗

arXiv2021

Lidar with Velocity: Correcting Moving Objects Point Cloud Distortion from Oscillating Scanning Lidars by Fusion with Camera

Lidar point cloud distortion from moving object is an important problem in autonomous driving, and recently becomes even more demanding with the emerging of newer lidars, which feature back-and-forth scanning patterns. Accurately estimating moving object velocity would not only provide a tracking capability but also correct the point cloud distortion with more accurate description of the moving object. Since lidar measures the time-of-flight distance but with a sparse angular resolution, the measurement is precise in the radial measurement but lacks angularly. Camera on the other hand provides a dense angular resolution. In this paper, Gaussian-based lidar and camera fusion is proposed to estimate the full velocity and correct the lidar distortion. A probabilistic Kalman-filter framework is provided to track the moving objects, estimate their velocities and simultaneously correct the point clouds distortions. The framework is evaluated on real road data and the fusion method outperforms the traditional ICP-based and point-cloud only method. The complete working framework is open-sourced (https://github.com/ISEE-Technology/lidar-with-velocity) to accelerate the adoption of the emerging lidars.

cs.RO↗

arXiv2021

Efficient and Probabilistic Adaptive Voxel Mapping for Accurate Online LiDAR Odometry

This paper proposes an efficient and probabilistic adaptive voxel mapping method for LiDAR odometry. The map is a collection of voxels; each contains one plane (or edge) feature that enables the probabilistic representation of the environment and accurate registration of a new LiDAR scan. We further analyze the need for coarse-to-fine voxel mapping and then use a novel voxel map organized by a Hash table and octrees to build and update the map efficiently. We apply the proposed voxel map to an iterated extended Kalman filter and construct a maximum a posteriori probability problem for pose estimation. Experiments on the open KITTI dataset show the high accuracy and efficiency of our method compared to other state-of-the-art methods. Outdoor experiments on unstructured environments with non-repetitive scanning LiDARs further verify the adaptability of our mapping method to different environments and LiDAR scanning patterns. Our codes and dataset are open-sourced on Github

cs.RO↗

arXiv2021

Pixel-level Extrinsic Self Calibration of High Resolution LiDAR and Camera in Targetless Environments

In this letter, we present a novel method for automatic extrinsic calibration of high-resolution LiDARs and RGB cameras in targetless environments. Our approach does not require checkerboards but can achieve pixel-level accuracy by aligning natural edge features in the two sensors. On the theory level, we analyze the constraints imposed by edge features and the sensitivity of calibration accuracy with respect to edge distribution in the scene. On the implementation level, we carefully investigate the physical measuring principles of LiDARs and propose an efficient and accurate LiDAR edge extraction method based on point cloud voxel cutting and plane fitting. Due to the edges' richness in natural scenes, we have carried out experiments in many indoor and outdoor scenes. The results show that this method has high robustness, accuracy, and consistency. It can promote the research and application of the fusion between LiDAR and camera. We have open-sourced our code on GitHub to benefit the community.

cs.RO↗

arXiv2020

CamVox: A Low-cost and Accurate Lidar-assisted Visual SLAM System

Combining lidar in camera-based simultaneous localization and mapping (SLAM) is an effective method in improving overall accuracy, especially at a large scale outdoor scenario. Recent development of low-cost lidars (e.g. Livox lidar) enable us to explore such SLAM systems with lower budget and higher performance. In this paper we propose CamVox by adapting Livox lidars into visual SLAM (ORB-SLAM2) by exploring the lidars' unique features. Based on the non-repeating nature of Livox lidars, we propose an automatic lidar-camera calibration method that will work in uncontrolled scenes. The long depth detection range also benefit a more efficient mapping. Comparison of CamVox with visual SLAM (VINS-mono) and lidar SLAM (LOAM) are evaluated on the same dataset to demonstrate the performance. We open sourced our hardware, code and dataset on GitHub.

cs.RO↗

arXiv2020

Low-cost Retina-like Robotic Lidars Based on Incommensurable Scanning

High performance lidars are essential in autonomous robots such as self-driving cars, automated ground vehicles and intelligent machines. Traditional mechanical scanning lidars offer superior performance in autonomous vehicles, but the potential mass application is limited by the inherent manufacturing difficulty. We propose a robotic lidar sensor based on incommensurable scanning that allows straightforward mass production and adoption in autonomous robots. Some unique features are additionally permitted by this incommensurable scanning. Similar to the fovea in human retina, this lidar features a peaked central angular density, enabling in applications that prefers eye-like attention. The incommensurable scanning method of this lidar could also provide a much higher resolution than conventional lidars which is beneficial in robotic applications such as sensor calibration. Examples making use of these advantageous features are demonstrated.

cs.RO↗

arXiv2014

Ultrafast Charge Transfer in Atomically Thin MoS2/WS2 Heterostructures

Van der Waals heterostructures have recently emerged as a new class of materials, where quantum coupling between stacked atomically thin two-dimensional (2D) layers, including graphene, hexagonal-boron nitride, and transition metal dichalcogenides (MX2), give rise to fascinating new phenomena. MX2 heterostructures are particularly exciting for novel optoelectronic and photovoltaic applications, because 2D MX2 monolayers can have an optical bandgap in the near-infrared to visible spectral range and exhibit extremely strong light-matter interactions. Theory predicts that many stacked MX2 heterostructures form type-II semiconductor heterojunctions that facilitate efficient electron-hole separation for light detection and harvesting. Here we report the first experimental observation of ultrafast charge transfer in photo-excited MoS2/WS2 heterostructures using both photoluminescence mapping and femtosecond (fs) pump-probe spectroscopy. We show that hole transfer from the MoS2 layer to the WS2 layer takes place within 50 fs after optical excitation, a remarkable rate for van der Waals coupled 2D layers. Such ultrafast charge transfer in van der Waals heterostructures can enable novel 2D devices for optoelectronics and light harvesting.

cond-mat.mes-hall↗

arXiv2014

Ultrafast Generation of Pseudo-magnetic Field for Valley Excitons in WSe2 Monolayers

A new degree of freedom, the valley pseudospin, emerges in atomically thin two-dimensional transition metal dichalcogenides (MX2) and has attracted great scientific interest. The capability to manipulate the valley pseudospin, in analogy to the control of spin in spintronics, can open up exciting opportunities in valleytronics. Here we demonstrate that an ultrafast and ultrahigh valley pseudomagnetic field can be generated using circularly polarized femtosecond pulses to selectively control the valley degree of freedom in monolayer MX2. Employing ultrafast pump-probe spectroscopy, we observed a pure and valley-selective optical Stark effect in WSe2 monolayers from the non-resonant pump, which instantaneously lift the degeneracy of valley exciton transitions without any dissipation. The strength of the optical Stark effect scales linearly with both the pump intensity and the inverse of pump detuning. An energy splitting more than 10 meV between the K and K_prime valley transitions can be achieved, which corresponds to an effective pseudomagnetic field over 170 Tesla. Our study demonstrates efficient and ultrafast control of the valley excitons with optical light, and opens up the possibility to coherent manipulate the valley polarization for quantum information applications.

cond-mat.mes-hall↗

arXiv2013

Systematic Determination of Absolute Absorption Cross-section of Individual Carbon Nanotubes

Determination of optical absorption cross-section is always among the central importance of understanding a material. However its realization on individual nanostructures, such as carbon nanotubes, is experimentally challenging due to the small extinction signal using conventional transmission measurements. Here we develop a technique based on polarization manipulation to enhance the sensitivity of single-nanotube absorption spectroscopy by two-orders of magnitude. We systematically determine absorption cross-section over broad spectral range at single-tube level for more than 50 chirality-defined single-walled nanotubes. Our data reveals chirality-dependent one-dimensional photo-physics through the behaviours of exciton oscillator strength and lifetime. We also establish an empirical formula to predict absorption spectrum of any nanotube, which provides the foundation to determine quantum efficiencies in important photoluminescence and photovoltaic processes.

cond-mat.mes-hall↗

arXiv2013

Intermolecular interactions and substrate effects for an adamantane monolayer on the Au(111) surface

We study theoretically and experimentally the infrared (IR) spectrum of an adamantane monolayer on a Au(111) surface. Using a new STM-based IR spectroscopy technique (IRSTM) we are able to measure both the nanoscale structure of an adamantane monolayer on Au(111) as well as its infrared spectrum, while DFT-based ab initio calculations allow us to interpret the microscopic vibrational dynamics revealed by our measurements. We find that the IR spectrum of an adamantane monolayer on Au(111) is substantially modified with respect to the gas-phase IR spectrum. The first modification is caused by the adamantane--adamantane interaction due to monolayer packing and it reduces the IR intensity of the 2912 cm$^{-1}$ peak (gas phase) by a factor of 3.5. The second modification originates from the adamantane--gold interaction and it increases the IR intensity of the 2938 cm$^{-1}$ peak (gas phase) by a factor of 2.6, and reduces its frequency by 276 cm$^{-1}$. We expect that the techniques described here can be used for an independent estimate of substrate effects and intermolecular interactions in other diamondoid molecules, and for other metallic substrates.

cond-mat.mes-hall↗

arXiv2013

High-throughput Imaging and Spectroscopy of Individual Carbon Nanotubes in Devices with Light Microscopy

Two paramount challenges in carbon nanotube research are achieving chirality-controlled synthesis and understanding chirality-dependent device physics. High-throughput and in-situ chirality and electronic structural characterization of individual carbon nanotubes is crucial for addressing these challenges. Optical imaging and spectroscopy has unparalleled throughput and specificity, but its realization for single nanotubes on substrates or in devices has long been an outstanding challenge. Here we demonstrate video-rate imaging and in-situ spectroscopy of individual carbon nanotubes on various substrates and in functional devices using a novel high-contrast polarization-based optical microscopy. Our technique enables the complete chirality profiling of hundreds of as-grown carbon nanotubes. In addition, we in-situ monitor nanotube electronic structure in active field-effect devices, and observe that high-order nanotube optical resonances are dramatically broadened by electrostatic doping. This unexpected behaviour points to strong interband electron-electron scattering processes that can dominate ultrafast dynamics of excited states in carbon nanotubes.

cond-mat.mes-hall↗