Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach

Autonomous vehicle manufacturers recognize that LiDAR provides accurate 3D views and precise distance measures under highly uncertain driving conditions. Its practical implementation, however, remains costly. This paper investigates the optimal LiDAR configuration problem to achieve utility maximization. We use the perception area and non-detectable subspace to construct the design procedure as solving a min-max optimization problem and propose a bio-inspired measure -- volume to surface area ratio (VSR) -- as an easy-to-evaluate cost function representing the notion of the size of the non-detectable subspaces of a given configuration. We then adopt a cuboid-based approach to show that the proposed VSR-based measure is a well-suited proxy for object detection rate. It is found that the Artificial Bee Colony evolutionary algorithm yields a tractable cost function computation. Our experiments highlight the effectiveness of our proposed VSR measure in terms of cost-effectiveness configuration as well as providing insightful analyses that can improve the design of AV systems.Comment: 7 pages including the references, accepted by International Conference on Robotics and Automation (ICRA), 201

DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments

Simultaneous Localization and Mapping (SLAM) is considered to be a fundamental capability for intelligent mobile robots. Over the past decades, many impressed SLAM systems have been developed and achieved good performance under certain circumstances. However, some problems are still not well solved, for example, how to tackle the moving objects in the dynamic environments, how to make the robots truly understand the surroundings and accomplish advanced tasks. In this paper, a robust semantic visual SLAM towards dynamic environments named DS-SLAM is proposed. Five threads run in parallel in DS-SLAM: tracking, semantic segmentation, local mapping, loop closing, and dense semantic map creation. DS-SLAM combines semantic segmentation network with moving consistency check method to reduce the impact of dynamic objects, and thus the localization accuracy is highly improved in dynamic environments. Meanwhile, a dense semantic octo-tree map is produced, which could be employed for high-level tasks. We conduct experiments both on TUM RGB-D dataset and in the real-world environment. The results demonstrate the absolute trajectory accuracy in DS-SLAM can be improved by one order of magnitude compared with ORB-SLAM2. It is one of the state-of-the-art SLAM systems in high-dynamic environments. Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLAMComment: 7 pages, accepted at the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018). Now the code is available at our github: https://github.com/ivipsourcecode/DS-SLA

Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving

The past few years have witnessed an increasing interest in improving the perception performance of LiDARs on autonomous vehicles. While most of the existing works focus on developing new deep learning algorithms or model architectures, we study the problem from the physical design perspective, i.e., how different placements of multiple LiDARs influence the learning-based perception. To this end, we introduce an easy-to-compute information-theoretic surrogate metric to quantitatively and fast evaluate LiDAR placement for 3D detection of different types of objects. We also present a new data collection, detection model training and evaluation framework in the realistic CARLA simulator to evaluate disparate multi-LiDAR configurations. Using several prevalent placements inspired by the designs of self-driving companies, we show the correlation between our surrogate metric and object detection performance of different representative algorithms on KITTI through extensive experiments, validating the effectiveness of our LiDAR placement evaluation approach. Our results show that sensor placement is non-negligible in 3D point cloud-based object detection, which will contribute up to 10% performance discrepancy in terms of average precision in challenging 3D object detection settings. We believe that this is one of the first studies to quantitatively investigate the influence of LiDAR placement on perception performance. The code is available on https://github.com/HanjiangHu/Multi-LiDAR-Placement-for-3D-Detection.Comment: CVPR 2022 camera-ready version:15 pages, 14 figures, 9 table

Identification of transcriptome induced in roots of maize seedlings at the late stage of waterlogging

<p>Abstract</p> <p>Background</p> <p>Plants respond to low oxygen stress, particularly that caused by waterlogging, by altering transcription and translation. Previous studies have mostly focused on revealing the mechanism of the response at the early stage, and there is limited information about the transcriptional profile of genes in maize roots at the late stage of waterlogging. The genetic basis of waterlogging tolerance is largely unknown. In this study, the transcriptome at the late stage of waterlogging was assayed in root cells of the tolerant inbred line HZ32, using suppression subtractive hybridization (SSH). A forward SSH library using RNA populations from four time points (12 h, 16 h, 20 h and 24 h) after waterlogging treatment was constructed to reveal up-regulated genes, and transcriptional and linkage data was integrated to identify candidate genes for waterlogging tolerance.</p> <p>Results</p> <p>Reverse Northern analysis of a set of 768 cDNA clones from the SSH library revealed a large number of genes were up-regulated by waterlogging. A total of 465 ESTs were assembled into 296 unigenes. Bioinformatic analysis revealed that the genes were involved in complex pathways, such as signal transduction, protein degradation, ion transport, carbon and amino acid metabolism, and transcriptional and translational regulation, and might play important roles at the late stage of the response to waterlogging. A significant number of unigenes were of unknown function. Approximately 67% of the unigenes could be aligned on the maize genome and 63 of them were co-located within reported QTLs.</p> <p>Conclusion</p> <p>The late response to waterlogging in maize roots involves a broad spectrum of genes, which are mainly associated with two response processes: defense at the early stage and adaption at the late stage. Signal transduction plays a key role in activating genes related to the tolerance mechanism for survival during prolonged waterlogging. The crosstalk between carbon and amino acid metabolism reveals that amino acid metabolism performs two main roles at the late stage: the regulation of cytoplasmic pH and energy supply through breakdown of the carbon skeleton.</p

Pixel-wise Smoothing for Certified Robustness against Camera Motion Perturbations

In recent years, computer vision has made remarkable advancements in autonomous driving and robotics. However, it has been observed that deep learning-based visual perception models lack robustness when faced with camera motion perturbations. The current certification process for assessing robustness is costly and time-consuming due to the extensive number of image projections required for Monte Carlo sampling in the 3D camera motion space. To address these challenges, we present a novel, efficient, and practical framework for certifying the robustness of 3D-2D projective transformations against camera motion perturbations. Our approach leverages a smoothing distribution over the 2D pixel space instead of in the 3D physical space, eliminating the need for costly camera motion sampling and significantly enhancing the efficiency of robustness certifications. With the pixel-wise smoothed classifier, we are able to fully upper bound the projection errors using a technique of uniform partitioning in camera motion space. Additionally, we extend our certification framework to a more general scenario where only a single-frame point cloud is required in the projection oracle. This is achieved by deriving Lipschitz-based approximated partition intervals. Through extensive experimentation, we validate the trade-off between effectiveness and efficiency enabled by our proposed method. Remarkably, our approach achieves approximately 80% certified accuracy while utilizing only 30% of the projected image frames.Comment: 32 pages, 5 figures, 13 table

TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models

The full potential of large pretrained models remains largely untapped in control domains like robotics. This is mainly because of the scarcity of data and the computational challenges associated with training or fine-tuning these large models for such applications. Prior work mainly emphasizes effective pretraining of large models for decision-making, with little exploration into how to perform data-efficient continual adaptation of these models for new tasks. Recognizing these constraints, we introduce TAIL (Task-specific Adapters for Imitation Learning), a framework for efficient adaptation to new control tasks. Inspired by recent advancements in parameter-efficient fine-tuning in language domains, we explore efficient fine-tuning techniques -- e.g., Bottleneck Adapters, P-Tuning, and Low-Rank Adaptation (LoRA) -- in TAIL to adapt large pretrained models for new tasks with limited demonstration data. Our extensive experiments in large-scale language-conditioned manipulation tasks comparing prevalent parameter-efficient fine-tuning techniques and adaptation baselines suggest that TAIL with LoRA can achieve the best post-adaptation performance with only 1\% of the trainable parameters of full fine-tuning, while avoiding catastrophic forgetting and preserving adaptation plasticity in continual learning settings.Comment: 21 pages, 8 figures, 8 table

Hidden charmonium decays of spin-2 partner of X(3872)X(3872)

The Belle collaboration recently reported a promising candidate for the spin-2 $D^*\bar{D}^*$ partner of the $X(3872)$, called the $X_2$ for short, having a mass of $(4014.3 \pm 4.0 \pm 1.5)~\mathrm{MeV}$ and a width of $(4 \pm 11 \pm 6)~\mathrm{MeV}$. Assuming the $X_2$ as a pure molecule of the $D^*\bar{D}^*$, we calculated in detail the hidden charmonium decays of the $X_2 \to J/\psi V$ and $X_2\to\eta_cP$ via the intermediate meson loops, where $V = \rho^0\,,\omega$ and $P= \pi^0\,,\eta\,,\eta'$. The results indicate that the decay widths are strongly dependent on the $X_2$ mass. At present center value of the mass $4014.3~\mathrm{MeV}$, the width for the $X_2\to J/\psi\rho^0$ is predicted to be a few tens of keV, while it is on the order of $10^{2\text{-}3}~\mathrm{keV}$ for the $X_2\to J/ \psi\omega$; the predicted width for the $X_2\to \eta_c \pi^0$ is about a few keV, while the widths for $X_2\to\eta_c\eta$ and $\eta_c\eta'$ are around a few tens and tenths of keV, respectively. We also investigated the dependence of the ratios between these widths on the $X_2$ mass and on the $\eta$-$\eta'$ mixing angle, which may be good quantities for experiments. We hope that the present calculations would be checked experimentally in the future.Comment: 9 pages, 12 figures, accepted by PR

Comparative transcriptomics uncovers alternative splicing changes and signatures of selection from maize improvement

Background: Alternative splicing (AS) is an important regulatory mechanism that greatly contributes to eukaryotic transcriptome diversity. A substantial amount of evidence has demonstrated that AS complexity is relevant to eukaryotic evolution, development, adaptation, and complexity. In this study, six teosinte and ten maize transcriptomes were sequenced to analyze AS changes and signatures of selection in maize domestication and improvement. Results In maize and teosinte, 13,593 highly conserved genes, including 12,030 multiexonic genes, were detected. By identifying AS isoforms from mutliexonic genes, we found that AS types were not significantly different between maize and teosinte. In addition, the two main AS types (intron retention and alternative acceptor) contributed to more than 60% of the AS events in the two species, but the average unique AS events per each alternatively spliced gene in maize (4.12) was higher than that in teosinte (2.26). Moreover, 94 genes generating 98 retained introns with transposable element (TE) sequences were detected in maize, which is far more than 9 retained introns with TEs detected in teosinte. This indicates that TE insertion might be an important mechanism for intron retention in maize. Additionally, the AS levels of 3864 genes were significantly different between maize and teosinte. Of these, 151 AS level-altered genes that are involved in transcriptional regulation and in stress responses are located in regions that have been targets of selection during maize improvement. These genes were inferred to be putatively improved genes. Conclusions We suggest that both maize and teosinte share similar AS mechanisms, but more genes have increased AS complexity during domestication from teosinte to maize. Importantly, a subset of AS level-increased genes that encode transcription factors and stress-responsive proteins may have been selected during maize improvement