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

SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments

Different environments pose a great challenge to the outdoor robust visual perception for long-term autonomous driving and the generalization of learning-based algorithms on different environmental effects is still an open problem. Although monocular depth prediction has been well studied recently, there is few work focusing on the robust learning-based depth prediction across different environments, e.g. changing illumination and seasons, owing to the lack of such a multi-environment real-world dataset and benchmark. To this end, the first cross-season monocular depth prediction dataset and benchmark SeasonDepth is built based on CMU Visual Localization dataset. To benchmark the depth estimation performance under different environments, we investigate representative and recent state-of-the-art open-source supervised, self-supervised and domain adaptation depth prediction methods from KITTI benchmark using several newly-formulated metrics. Through extensive experimental evaluation on the proposed dataset, the influence of multiple environments on performance and robustness is analyzed qualitatively and quantitatively, showing that the long-term monocular depth prediction is still challenging even with fine-tuning. We further give promising avenues that self-supervised training and stereo geometry constraint help to enhance the robustness to changing environments. The dataset is available on https://seasondepth.github.io, and benchmark toolkit is available on https://github.com/SeasonDepth/SeasonDepth.Comment: 19 pages, 13 figure

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

Characterization and Expression Patterns of Auxin Response Factors in Wheat

Auxin response factors (ARFs) are important transcription factors involved in both the auxin signaling pathway and the regulatory development of various plant organs. In this study, 23 TaARF members encoded by a total of 68 homeoalleles were isolated from 18 wheat chromosomes (excluding chromosome 4). The TaARFs, including their conserved domains, exon/intron structures, related microRNAs, and alternative splicing (AS) variants, were then characterized. Phylogenetic analysis revealed that members of the TaARF family share close homology with ARFs in other grass species. qRT-PCR analyses revealed that 20 TaARF members were expressed in different organs and tissues and that the expression of some members significantly differed in the roots, stems, and leaves of wheat seedlings in response to exogenous auxin treatment. Moreover, protein network analyses and co-expression results showed that TaTIR1–TaARF15/18/19–TaIAA13 may interact at both the protein and genetic levels. The results of subsequent evolutionary analyses showed that three transcripts of TaARF15 in the A subgenome of wheat exhibited high evolutionary rate and underwent positive selection. Transgenic analyses indicated that TaARF15-A.1 promoted the growth of roots and leaves of Arabidopsis thaliana and was upregulated in the overexpression plants after auxin treatment. Our results will provide reference information for subsequent research and utilization of the TaARF gene family

Development of Sequence-Tagged Site Marker Set for Identification of J, JS, and St Sub-genomes of Thinopyrum intermedium in Wheat Background

Thinopyrum intermedium (2n = 6x = 42, JJJSJSStSt) is one of the important resources for the wheat improvement. So far, a few Th. intermedium (Thi)-specific molecular markers have been reported, but the number is far from enough to meet the need of identifying alien fragments in wheat-Th. intermedium hybrids. In this study, 5,877,409 contigs were assembled using the Th. intermedium genotyping-by-sequencing (GBS) data. We obtained 5,452 non-redundant contigs containing mapped Thi-GBS markers with less than 20% similarity to the wheat genome and developed 2,019 sequence-tagged site (STS) molecular markers. Among the markers designed, 745 Thi-specific markers with amplification products in Th. intermedium but not in eight wheat landraces were further selected. The distribution of these markers in different homologous groups of Th. intermedium varied from 47 (7/12/28 on 6J/6St/6JS) to 183 (54/62/67 on 7J/7St/7JS). Furthermore, the effectiveness of these Thi-specific markers was verified using wheat-Th. intermedium partial amphidiploids, addition lines, substitution lines, and translocation lines. Markers developed in this study provide a convenient, rapid, reliable, and economical method for identifying Th. intermedium chromosomes in wheat. In addition, this set of Thi-specific markers can also be used to estimate genetic and physical locations of Th. intermedium chromatin in the introgression lines, thus providing valuable information for follow-up studies such as alien gene mining