New submissions for Fri, 18 Feb 22

[zhuhu00]

Keyword: SLAM

Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments

• Authors: Jinkun Wang, Fanfei Chen, Yewei Huang, John McConnell, Tixiao Shan, Brendan Englot
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.08359
• Pdf link: https://arxiv.org/pdf/2202.08359
• Abstract We consider the problem of autonomous mobile robot exploration in an unknown environment, taking into account a robot's coverage rate, map uncertainty, and state estimation uncertainty. This paper presents a novel exploration framework for underwater robots operating in cluttered environments, built upon simultaneous localization and mapping (SLAM) with imaging sonar. The proposed system comprises path generation, place recognition forecasting, belief propagation and utility evaluation using a virtual map, which estimates the uncertainty associated with map cells throughout a robot's workspace. We evaluate the performance of this framework in simulated experiments, showing that our algorithm maintains a high coverage rate during exploration while also maintaining low mapping and localization error. The real-world applicability of our framework is also demonstrated on an underwater remotely operated vehicle (ROV) exploring a harbor environment.

  LiDAR-Inertial 3D SLAM with Plane Constraint for Multi-story Building

• Authors: Jiashi Zhang, Chengyang Zhang, Jun Wu, Jianxiang Jin, Qiuguo Zhu
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.08487
• Pdf link: https://arxiv.org/pdf/2202.08487
• Abstract The ubiquitous planes and structural consistency are the most apparent features of indoor multi-story Buildings compared with outdoor environments. In this paper, we propose a tightly coupled LiDAR-Inertial 3D SLAM framework with plane features for the multi-story building. The framework we proposed is mainly composed of three parts: tightly coupled LiDAR-Inertial odometry, extraction of representative planes of the structure, and factor graph optimization. By building a local map and inertial measurement unit (IMU) pre-integration, we get LiDAR scan-to-local-map matching and IMU measurements, respectively. Minimize the joint cost function to obtain the LiDAR-Inertial odometry information. Once a new keyframe is added to the graph, all the planes of this keyframe that can represent structural features are extracted to find the constraint between different poses and stories. A keyframe-based factor graph is conducted with the constraint of planes, and LiDAR-Inertial odometry for keyframe poses refinement. The experimental results show that our algorithm has outstanding performance in accuracy compared with the state-of-the-art algorithms.

  Keyword: Visual inertial

  There is no result

  Keyword: livox

  There is no result

  Keyword: loam

  There is no result

  Keyword: Visual inertial odometry

  There is no result

  Keyword: lidar

  LiDAR-Inertial 3D SLAM with Plane Constraint for Multi-story Building

• Authors: Jiashi Zhang, Chengyang Zhang, Jun Wu, Jianxiang Jin, Qiuguo Zhu
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.08487
• Pdf link: https://arxiv.org/pdf/2202.08487
• Abstract The ubiquitous planes and structural consistency are the most apparent features of indoor multi-story Buildings compared with outdoor environments. In this paper, we propose a tightly coupled LiDAR-Inertial 3D SLAM framework with plane features for the multi-story building. The framework we proposed is mainly composed of three parts: tightly coupled LiDAR-Inertial odometry, extraction of representative planes of the structure, and factor graph optimization. By building a local map and inertial measurement unit (IMU) pre-integration, we get LiDAR scan-to-local-map matching and IMU measurements, respectively. Minimize the joint cost function to obtain the LiDAR-Inertial odometry information. Once a new keyframe is added to the graph, all the planes of this keyframe that can represent structural features are extracted to find the constraint between different poses and stories. A keyframe-based factor graph is conducted with the constraint of planes, and LiDAR-Inertial odometry for keyframe poses refinement. The experimental results show that our algorithm has outstanding performance in accuracy compared with the state-of-the-art algorithms.

  Keyword: loop detection

  There is no result

  Keyword: autonomous driving

  Real Time Motion Planning Using Constrained Iterative Linear Quadratic Regulator for On-Road Self-Driving

• Authors: Changxi You
• Subjects: Robotics (cs.RO); Optimization and Control (math.OC)
• Arxiv link: https://arxiv.org/abs/2202.08400
• Pdf link: https://arxiv.org/pdf/2202.08400
• Abstract Collision avoidance is one of the most challenging tasks people need to consider for developing the self-driving technology. In this paper we propose a new spatiotemporal motion planning algorithm that efficiently solves a constrained nonlinear optimal control problem using the iterative linear quadratic regulator (iLQR), which takes into account the uncertain driving behaviors of the traffic vehicles and minimizes the collision risks between the self-driving vehicle (referred to as the "ego" vehicle) and the traffic vehicles such that the ego vehicle is able to maintain sufficiently large distances to all the surrounding vehicles for achieving the desired collision avoidance maneuver in traffic. To this end, we introduce the concept of the "collision polygon" for computing the minimum distances between the ego vehicle and the traffic vehicles, and provide two different solutions for designing the constraints of the motion planning problem by properly modeling the behaviors of the traffic vehicles in order to evaluate the collision risk. Finally, the iLQR motion planning algorithm is validated in multiple real-time tasks for collision avoidance using both a simulator and a level-3 autonomous driving test platform.

  V2X-Sim: A Virtual Collaborative Perception Dataset for Autonomous Driving

• Authors: Yiming Li, Ziyan An, Zixun Wang, Yiqi Zhong, Siheng Chen, Chen Feng
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2202.08449
• Pdf link: https://arxiv.org/pdf/2202.08449
• Abstract Vehicle-to-everything (V2X), which denotes the collaboration between a vehicle and any entity in its surrounding, can fundamentally improve the perception in self-driving systems. As the individual perception rapidly advances, collaborative perception has made little progress due to the shortage of public V2X datasets. In this work, we present the V2X-Sim dataset, the first public large-scale collaborative perception dataset in autonomous driving. V2X-Sim provides: 1) well-synchronized recordings from roadside infrastructure and multiple vehicles at the intersection to enable collaborative perception, 2) multi-modality sensor streams to facilitate multi-modality perception, 3) diverse well-annotated ground truth to support various downstream tasks including detection, tracking, and segmentation. We seek to inspire research on multi-agent multi-modality multi-task perception, and our virtual dataset is promising to promote the development of collaborative perception before realistic datasets become widely available.

  CSCNet: Contextual Semantic Consistency Network for Trajectory Prediction in Crowded Spaces

• Authors: Beihao Xia, Conghao Wong, Qinmu Peng, Wei Yuan, Xinge You
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
• Arxiv link: https://arxiv.org/abs/2202.08506
• Pdf link: https://arxiv.org/pdf/2202.08506
• Abstract Trajectory prediction aims to predict the movement trend of the agents like pedestrians, bikers, vehicles. It is helpful to analyze and understand human activities in crowded spaces and widely applied in many areas such as surveillance video analysis and autonomous driving systems. Thanks to the success of deep learning, trajectory prediction has made significant progress. The current methods are dedicated to studying the agents' future trajectories under the social interaction and the sceneries' physical constraints. Moreover, how to deal with these factors still catches researchers' attention. However, they ignore the \textbf{Semantic Shift Phenomenon} when modeling these interactions in various prediction sceneries. There exist several kinds of semantic deviations inner or between social and physical interactions, which we call the "\textbf{Gap}". In this paper, we propose a \textbf{C}ontextual \textbf{S}emantic \textbf{C}onsistency \textbf{Net}work (\textbf{CSCNet}) to predict agents' future activities with powerful and efficient context constraints. We utilize a well-designed context-aware transfer to obtain the intermediate representations from the scene images and trajectories. Then we eliminate the differences between social and physical interactions by aligning activity semantics and scene semantics to cross the Gap. Experiments demonstrate that CSCNet performs better than most of the current methods quantitatively and qualitatively.

  Keyword: mapping

  Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments

• Authors: Jinkun Wang, Fanfei Chen, Yewei Huang, John McConnell, Tixiao Shan, Brendan Englot
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.08359
• Pdf link: https://arxiv.org/pdf/2202.08359
• Abstract We consider the problem of autonomous mobile robot exploration in an unknown environment, taking into account a robot's coverage rate, map uncertainty, and state estimation uncertainty. This paper presents a novel exploration framework for underwater robots operating in cluttered environments, built upon simultaneous localization and mapping (SLAM) with imaging sonar. The proposed system comprises path generation, place recognition forecasting, belief propagation and utility evaluation using a virtual map, which estimates the uncertainty associated with map cells throughout a robot's workspace. We evaluate the performance of this framework in simulated experiments, showing that our algorithm maintains a high coverage rate during exploration while also maintaining low mapping and localization error. The real-world applicability of our framework is also demonstrated on an underwater remotely operated vehicle (ROV) exploring a harbor environment.

  SWIM: Selective Write-Verify for Computing-in-Memory Neural Accelerators

• Authors: Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
• Subjects: Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2202.08395
• Pdf link: https://arxiv.org/pdf/2202.08395
• Abstract Computing-in-Memory architectures based on non-volatile emerging memories have demonstrated great potential for deep neural network (DNN) acceleration thanks to their high energy efficiency. However, these emerging devices can suffer from significant variations during the mapping process i.e., programming weights to the devices), and if left undealt with, can cause significant accuracy degradation. The non-ideality of weight mapping can be compensated by iterative programming with a write-verify scheme, i.e., reading the conductance and rewriting if necessary. In all existing works, such a practice is applied to every single weight of a DNN as it is being mapped, which requires extensive programming time. In this work, we show that it is only necessary to select a small portion of the weights for write-verify to maintain the DNN accuracy, thus achieving significant speedup. We further introduce a second derivative based technique SWIM, which only requires a single pass of forward and backpropagation, to efficiently select the weights that need write-verify. Experimental results on various DNN architectures for different datasets show that SWIM can achieve up to 10x programming speedup compared with conventional full-blown write-verify while attaining a comparable accuracy.

  MeNTT: A Compact and Efficient Processing-in-Memory Number Theoretic Transform (NTT) Accelerator

• Authors: Dai Li, Akhil Pakala, Kaiyuan Yang
• Subjects: Cryptography and Security (cs.CR); Systems and Control (eess.SY)
• Arxiv link: https://arxiv.org/abs/2202.08446
• Pdf link: https://arxiv.org/pdf/2202.08446
• Abstract Lattice-based cryptography (LBC) exploiting Learning with Errors (LWE) problems is a promising candidate for post-quantum cryptography. Number theoretic transform (NTT) is the latency- and energy- dominant process in the computation of LWE problems. This paper presents a compact and efficient in-MEmory NTT accelerator, named MeNTT, which explores optimized computation in and near a 6T SRAM array. Specifically-designed peripherals enable fast and efficient modular operations. Moreover, a novel mapping strategy reduces the data flow between NTT stages into a unique pattern, which greatly simplifies the routing among processing units (i.e., SRAM column in this work), reducing energy and area overheads. The accelerator achieves significant latency and energy reductions over prior arts.

  Multi-Objective Model Selection for Time Series Forecasting

• Authors: Oliver Borchert, David Salinas, Valentin Flunkert, Tim Januschowski, Stephan Günnemann
• Subjects: Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2202.08485
• Pdf link: https://arxiv.org/pdf/2202.08485
• Abstract Research on time series forecasting has predominantly focused on developing methods that improve accuracy. However, other criteria such as training time or latency are critical in many real-world applications. We therefore address the question of how to choose an appropriate forecasting model for a given dataset among the plethora of available forecasting methods when accuracy is only one of many criteria. For this, our contributions are two-fold. First, we present a comprehensive benchmark, evaluating 7 classical and 6 deep learning forecasting methods on 44 heterogeneous, publicly available datasets. The benchmark code is open-sourced along with evaluations and forecasts for all methods. These evaluations enable us to answer open questions such as the amount of data required for deep learning models to outperform classical ones. Second, we leverage the benchmark evaluations to learn good defaults that consider multiple objectives such as accuracy and latency. By learning a mapping from forecasting models to performance metrics, we show that our method PARETOSELECT is able to accurately select models from the Pareto front -- alleviating the need to train or evaluate many forecasting models for model selection. To the best of our knowledge, PARETOSELECT constitutes the first method to learn default models in a multi-objective setting.

  Domain Adaptation for Underwater Image Enhancement via Content and Style Separation

• Authors: Yu-Wei Chen, Soo-Chang Pei
• Subjects: Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2202.08537
• Pdf link: https://arxiv.org/pdf/2202.08537
• Abstract Underwater image suffer from color cast, low contrast and hazy effect due to light absorption, refraction and scattering, which degraded the high-level application, e.g, object detection and object tracking. Recent learning-based methods demonstrate astonishing performance on underwater image enhancement, however, most of these works use synthesis pair data for supervised learning and ignore the domain gap to real-world data. In this paper, we propose a domain adaptation framework for underwater image enhancement via content and style separation, we assume image could be disentangled to content and style latent, and image could be clustered to the sub-domain of associated style in latent space, the goal is to build up the mapping between underwater style latent and clean one. Different from prior works of domain adaptation for underwater image enhancement, which target to minimize the latent discrepancy of synthesis and real-world data, we aim to distinguish style latent from different sub-domains. To solve the problem of lacking pair real-world data, we leverage synthesis to real image-to-image translation to obtain pseudo real underwater image pairs for supervised learning, and enhancement can be achieved by input content and clean style latent into generator. Our model provide a user interact interface to adjust different enhanced level by latent manipulation. Experiment on various public real-world underwater benchmarks demonstrate that the proposed framework is capable to perform domain adaptation for underwater image enhancement and outperform various state-of-the-art underwater image enhancement algorithms in quantity and quality. The model and source code are available at https://github.com/fordevoted/UIESS

  Keyword: localization

  Anomalib: A Deep Learning Library for Anomaly Detection

• Authors: Samet Akcay, Dick Ameln, Ashwin Vaidya, Barath Lakshmanan, Nilesh Ahuja, Utku Genc
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
• Arxiv link: https://arxiv.org/abs/2202.08341
• Pdf link: https://arxiv.org/pdf/2202.08341
• Abstract This paper introduces anomalib, a novel library for unsupervised anomaly detection and localization. With reproducibility and modularity in mind, this open-source library provides algorithms from the literature and a set of tools to design custom anomaly detection algorithms via a plug-and-play approach. Anomalib comprises state-of-the-art anomaly detection algorithms that achieve top performance on the benchmarks and that can be used off-the-shelf. In addition, the library provides components to design custom algorithms that could be tailored towards specific needs. Additional tools, including experiment trackers, visualizers, and hyper-parameter optimizers, make it simple to design and implement anomaly detection models. The library also supports OpenVINO model optimization and quantization for real-time deployment. Overall, anomalib is an extensive library for the design, implementation, and deployment of unsupervised anomaly detection models from data to the edge.

  Virtual Maps for Autonomous Exploration of Cluttered Underwater Environments

• Authors: Jinkun Wang, Fanfei Chen, Yewei Huang, John McConnell, Tixiao Shan, Brendan Englot
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2202.08359
• Pdf link: https://arxiv.org/pdf/2202.08359
• Abstract We consider the problem of autonomous mobile robot exploration in an unknown environment, taking into account a robot's coverage rate, map uncertainty, and state estimation uncertainty. This paper presents a novel exploration framework for underwater robots operating in cluttered environments, built upon simultaneous localization and mapping (SLAM) with imaging sonar. The proposed system comprises path generation, place recognition forecasting, belief propagation and utility evaluation using a virtual map, which estimates the uncertainty associated with map cells throughout a robot's workspace. We evaluate the performance of this framework in simulated experiments, showing that our algorithm maintains a high coverage rate during exploration while also maintaining low mapping and localization error. The real-world applicability of our framework is also demonstrated on an underwater remotely operated vehicle (ROV) exploring a harbor environment.