Laser spectroscopy of gas confined in nanoporous materials

We show that high-resolution laser spectroscopy can probe surface interactions of gas confined in nano-cavities of porous materials. We report on strong line broadening and unfamiliar lineshapes due to tight confinement, as well as signal enhancement due to multiple photon scattering. This new domain of laser spectroscopy constitute a challenge for the theory of collisions and spectroscopic lineshapes, and open for new ways of analyzing porous materials and processes taking place therein.Comment: 4 pages, 3 figures, partly presented orally at 7th International Conference on Tunable Diode Laser Spectroscopy (TDLS), July 2009 in Zermatt, Switzerland; Accepted for Applied Physics Letters (December 2009

Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap

Global point cloud registration is essential in many robotics tasks like loop closing and relocalization. Unfortunately, the registration often suffers from the low overlap between point clouds, a frequent occurrence in practical applications due to occlusion and viewpoint change. In this paper, we propose a graph-theoretic framework to address the problem of global point cloud registration with low overlap. To this end, we construct a consistency graph to facilitate robust data association and employ graduated non-convexity (GNC) for reliable pose estimation, following the state-of-the-art (SoTA) methods. Unlike previous approaches, we use semantic cues to scale down the dense point clouds, thus reducing the problem size. Moreover, we address the ambiguity arising from the consistency threshold by constructing a pyramid graph with multi-level consistency thresholds. Then we propose a cascaded gradient ascend method to solve the resulting densest clique problem and obtain multiple pose candidates for every consistency threshold. Finally, fast geometric verification is employed to select the optimal estimation from multiple pose candidates. Our experiments, conducted on a self-collected indoor dataset and the public KITTI dataset, demonstrate that our method achieves the highest success rate despite the low overlap of point clouds and low semantic quality. We have open-sourced our code https://github.com/HKUST-Aerial-Robotics/Pagor for this project.Comment: Accepted by IROS202

Online Monocular Lane Mapping Using Catmull-Rom Spline

In this study, we introduce an online monocular lane mapping approach that solely relies on a single camera and odometry for generating spline-based maps. Our proposed technique models the lane association process as an assignment issue utilizing a bipartite graph, and assigns weights to the edges by incorporating Chamfer distance, pose uncertainty, and lateral sequence consistency. Furthermore, we meticulously design control point initialization, spline parameterization, and optimization to progressively create, expand, and refine splines. In contrast to prior research that assessed performance using self-constructed datasets, our experiments are conducted on the openly accessible OpenLane dataset. The experimental outcomes reveal that our suggested approach enhances lane association and odometry precision, as well as overall lane map quality. We have open-sourced our code1 for this project.Comment: Accepted by IROS202

Multi-Session, Localization-oriented and Lightweight LiDAR Mapping Using Semantic Lines and Planes

In this paper, we present a centralized framework for multi-session LiDAR mapping in urban environments, by utilizing lightweight line and plane map representations instead of widely used point clouds. The proposed framework achieves consistent mapping in a coarse-to-fine manner. Global place recognition is achieved by associating lines and planes on the Grassmannian manifold, followed by an outlier rejection-aided pose graph optimization for map merging. Then a novel bundle adjustment is also designed to improve the local consistency of lines and planes. In the experimental section, both public and self-collected datasets are used to demonstrate efficiency and effectiveness. Extensive results validate that our LiDAR mapping framework could merge multi-session maps globally, optimize maps incrementally, and is applicable for lightweight robot localization.Comment: Accepted by IROS202

FM-Fusion: Instance-aware Semantic Mapping Boosted by Vision-Language Foundation Models

Semantic mapping based on the supervised object detectors is sensitive to image distribution. In real-world environments, the object detection and segmentation performance can lead to a major drop, preventing the use of semantic mapping in a wider domain. On the other hand, the development of vision-language foundation models demonstrates a strong zero-shot transferability across data distribution. It provides an opportunity to construct generalizable instance-aware semantic maps. Hence, this work explores how to boost instance-aware semantic mapping from object detection generated from foundation models. We propose a probabilistic label fusion method to predict close-set semantic classes from open-set label measurements. An instance refinement module merges the over-segmented instances caused by inconsistent segmentation. We integrate all the modules into a unified semantic mapping system. Reading a sequence of RGB-D input, our work incrementally reconstructs an instance-aware semantic map. We evaluate the zero-shot performance of our method in ScanNet and SceneNN datasets. Our method achieves 40.3 mean average precision (mAP) on the ScanNet semantic instance segmentation task. It outperforms the traditional semantic mapping method significantly.Comment: Accepted by IEEE RA-

Less is More: Physical-enhanced Radar-Inertial Odometry

Radar offers the advantage of providing additional physical properties related to observed objects. In this study, we design a physical-enhanced radar-inertial odometry system that capitalizes on the Doppler velocities and radar cross-section information. The filter for static radar points, correspondence estimation, and residual functions are all strengthened by integrating the physical properties. We conduct experiments on both public datasets and our self-collected data, with different mobile platforms and sensor types. Our quantitative results demonstrate that the proposed radar-inertial odometry system outperforms alternative methods using the physical-enhanced components. Our findings also reveal that using the physical properties results in fewer radar points for odometry estimation, but the performance is still guaranteed and even improved, thus aligning with the ``less is more'' principle.Comment: Accepted by ICRA 202

G3Reg: Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model

This study introduces a novel framework, G3Reg, for fast and robust global registration of LiDAR point clouds. In contrast to conventional complex keypoints and descriptors, we extract fundamental geometric primitives including planes, clusters, and lines (PCL) from the raw point cloud to obtain low-level semantic segments. Each segment is formulated as a unified Gaussian Ellipsoid Model (GEM) by employing a probability ellipsoid to ensure the ground truth centers are encompassed with a certain degree of probability. Utilizing these GEMs, we then present a distrust-and-verify scheme based on a Pyramid Compatibility Graph for Global Registration (PAGOR). Specifically, we establish an upper bound, which can be traversed based on the confidence level for compatibility testing to construct the pyramid graph. Gradually, we solve multiple maximum cliques (MAC) for each level of the graph, generating numerous transformation candidates. In the verification phase, we adopt a precise and efficient metric for point cloud alignment quality, founded on geometric primitives, to identify the optimal candidate. The performance of the algorithm is extensively validated on three publicly available datasets and a self-collected multi-session dataset, without changing any parameter settings in the experimental evaluation. The results exhibit superior robustness and real-time performance of the G3Reg framework compared to state-of-the-art methods. Furthermore, we demonstrate the potential for integrating individual GEM and PAGOR components into other algorithmic frameworks to enhance their efficacy. To advance further research and promote community understanding, we have publicly shared the source code.Comment: Under revie

Contribution of CRISPRable DNA to human complex traits

CRISPR-Cas is a powerful genome editing tool for various species and human cell lines, widely used in many research areas including studying the mechanisms, targets, and gene therapies of human diseases. Recent developments have even allowed high-throughput genetic screening using the CRISPR system. However, due to the practical and ethical limitations in human gene editing research, little is known about whether CRISPR-editable DNA segments could influence human complex traits or diseases. Here, we investigated the human genomic regions condensed with different CRISPR Cas enzymes’ protospacer-adjacent motifs (PAMs). We found that Cas enzymes with GC-rich PAMs could interfere more with the genomic regions that harbor enriched heritability for human complex traits and diseases. The results linked GC content across the genome to the functional genomic elements in the heritability enrichment of human complex traits. We provide a genetic overview of the effects of high-throughput genome editing on human complex traits