New submissions for Thu, 6 Jan 22

[zhuhu00]

Keyword: SLAM

There is no result

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

There is no result

Keyword: loop detection

There is no result

Keyword: autonomous driving

There is no result

Keyword: mapping

Exploratory Analysis of Academic Collaborations between French and US

• Authors: George Panagopoulos, Michalis Vazirgiannis
• Subjects: Digital Libraries (cs.DL)
• Arxiv link: https://arxiv.org/abs/2201.01346
• Pdf link: https://arxiv.org/pdf/2201.01346
• Abstract International academic collaborations cultivate diversity in the research landscape and facilitate multiperspective methods, as the scope of each country's science depends on its needs, history, wealth etc. Moreover the quality of science differ significantly amongst nations\cite{king2004scientific}, which renders international collaborations a potential source to understand the dynamics between countries and their advancements. Analyzing these collaborations can reveal sharing expertise between two countries in different fields, the most well-known institutions of a nation, the overall success of collaborative efforts compared to local ones etc. Such analysis were initially performed using statistical metrics \cite{melin1996studying}, but network analysis has later proven much more expressive \cite{wagner2005mapping,gonzalez2008coauthorship}. In this exploratory analysis, we aim to examine the collaboration patterns between French and US institutions. Towards this, we capitalize on the Microsoft Academic Graph MAG \cite{sinha2015overview}, the largest open bibliographic dataset that contains detailed information for authors, publications and institutions. We use the coordinates of the world map to tally affiliations to France or USA. In cases where the coordinates of an affiliation were absent, we used its Wikipedia url and named entity recognition to identify the country of its address in the Wikipedia page. We need to stress that institute names have been volatile (due to University federations created) in the last decade in France, so this is a best effort trial. The results indicate an intensive and increasing scientific production in with , with certain institutions such as Harvard, MIT and CNRS standing out.

  RTNN: Accelerating Neighbor Search Using Hardware Ray Tracing

• Authors: Yuhao Zhu
• Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Graphics (cs.GR)
• Arxiv link: https://arxiv.org/abs/2201.01366
• Pdf link: https://arxiv.org/pdf/2201.01366
• Abstract Neighbor search is of fundamental important to many engineering and science fields such as physics simulation and computer graphics. This paper proposes to formulate neighbor search as a ray tracing problem and leverage the dedicated ray tracing hardware in recent GPUs for acceleration. We show that a naive mapping under-exploits the ray tracing hardware. We propose two performance optimizations, query scheduling and query partitioning, to tame the inefficiencies. Experimental results show 2.2X -- 65.0X speedups over existing neighbor search libraries on GPUs. The code is available at https://github.com/horizon-research/rtnn.

  Deep learning for location based beamforming with NLOS channels

• Authors: Luc Le Magoarou (IRT b-com), Taha Yassine (IRT b-com, INSA Rennes, IETR), Stéphane Paquelet (IRT b-com), Matthieu Crussière (IRT b-com, INSA Rennes, IETR)
• Subjects: Information Theory (cs.IT); Machine Learning (cs.LG); Social and Information Networks (cs.SI); Signal Processing (eess.SP)
• Arxiv link: https://arxiv.org/abs/2201.01386
• Pdf link: https://arxiv.org/pdf/2201.01386
• Abstract Massive MIMO systems are highly efficient but critically rely on accurate channel state information (CSI) at the base station in order to determine appropriate precoders. CSI acquisition requires sending pilot symbols which induce an important overhead. In this paper, a method whose objective is to determine an appropriate precoder from the knowledge of the user's location only is proposed. Such a way to determine precoders is known as location based beamforming. It allows to reduce or even eliminate the need for pilot symbols, depending on how the location is obtained. the proposed method learns a direct mapping from location to precoder in a supervised way. It involves a neural network with a specific structure based on random Fourier features allowing to learn functions containing high spatial frequencies. It is assessed empirically and yields promising results on realistic synthetic channels. As opposed to previously proposed methods, it allows to handle both line-of-sight (LOS) and non-line-of-sight (NLOS) channels.

  ADRA: Extending Digital Computing-in-Memory with Asymmetric Dual-Row-Activation

• Authors: Akul Malhotra, Atanu K. Saha, Chunguang Wang, Sumeet K. Gupta
• Subjects: Hardware Architecture (cs.AR)
• Arxiv link: https://arxiv.org/abs/2201.01509
• Pdf link: https://arxiv.org/pdf/2201.01509
• Abstract Computing in-memory (CiM) has emerged as an attractive technique to mitigate the von-Neumann bottleneck. Current digital CiM approaches for in-memory operands are based on multi-wordline assertion for computing bit-wise Boolean functions and arithmetic functions such as addition. However, most of these techniques, due to the many-to-one mapping of input vectors to bitline voltages, are limited to CiM of commutative functions, leaving out an important class of computations such as subtraction. In this paper, we propose a CiM approach, which solves the mapping problem through an asymmetric wordline biasing scheme, enabling (a) simultaneous single-cycle memory read and CiM of primitive Boolean functions (b) computation of any Boolean function and (c) CiM of non-commutative functions such as subtraction and comparison. While the proposed technique is technology-agnostic, we show its utility for ferroelectric transistor (FeFET)-based non-volatile memory. Compared to the standard near-memory methods (which require two full memory accesses per operation), we show that our method can achieve a full scale two-operand digital CiM using just one memory access, leading to a 23.2% - 72.6% decrease in energy-delay product (EDP).

  Identification of potential in diffusion equations from terminal observation: analysis and discrete approximation

• Authors: Zhengqi Zhang, Zhidong Zhang, Zhi Zhou
• Subjects: Numerical Analysis (math.NA)
• Arxiv link: https://arxiv.org/abs/2201.01563
• Pdf link: https://arxiv.org/pdf/2201.01563
• Abstract The aim of this paper is to study the recovery of a spatially dependent potential in a (sub)diffusion equation from overposed final time data. We construct a monotone operator one of whose fixed points is the unknown potential. The uniqueness of the identification is theoretically verified by using the monotonicity of the operator and a fixed point argument. Moreover, we show a conditional stability in Hilbert spaces under some suitable conditions on the problem data. Next, a completely discrete scheme is developed, by using Galerkin finite element method in space and finite difference method in time, and then a fixed point iteration is applied to reconstruct the potential. We prove the linear convergence of the iterative algorithm by the contraction mapping theorem, and present a thorough error analysis for the reconstructed potential. Our derived \textsl{a priori} error estimate provides a guideline to choose discretization parameters according to the noise level. The analysis relies heavily on some suitable nonstandard error estimates for the direct problem as well as the aforementioned conditional stability. Numerical experiments are provided to illustrate and complement our theoretical analysis.

  Keyword: localization

  DenseTact: Optical Tactile Sensor for Dense Shape Reconstruction

• Authors: Won Kyung Do, Monroe Kennedy III
• Subjects: Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
• Arxiv link: https://arxiv.org/abs/2201.01367
• Pdf link: https://arxiv.org/pdf/2201.01367
• Abstract Increasing the performance of tactile sensing in robots enables versatile, in-hand manipulation. Vision-based tactile sensors have been widely used as rich tactile feedback has been shown to be correlated with increased performance in manipulation tasks. Existing tactile sensor solutions with high resolution have limitations that include low accuracy, expensive components, or lack of scalability. In this paper, an inexpensive, scalable, and compact tactile sensor with high-resolution surface deformation modeling for surface reconstruction of the 3D sensor surface is proposed. By measuring the image from the fisheye camera, it is shown that the sensor can successfully estimate the surface deformation in real-time (1.8ms) by using deep convolutional neural networks. This sensor in its design and sensing abilities represents a significant step toward better object in-hand localization, classification, and surface estimation all enabled by high-resolution shape reconstruction.

  Fusing Convolutional Neural Network and Geometric Constraint for Image-based Indoor Localization

• Authors: Jingwei Song, Mitesh Patel, Maani Ghaffari
• Subjects: Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2201.01408
• Pdf link: https://arxiv.org/pdf/2201.01408
• Abstract This paper proposes a new image-based localization framework that explicitly localizes the camera/robot by fusing Convolutional Neural Network (CNN) and sequential images' geometric constraints. The camera is localized using a single or few observed images and training images with 6-degree-of-freedom pose labels. A Siamese network structure is adopted to train an image descriptor network, and the visually similar candidate image in the training set is retrieved to localize the testing image geometrically. Meanwhile, a probabilistic motion model predicts the pose based on a constant velocity assumption. The two estimated poses are finally fused using their uncertainties to yield an accurate pose prediction. This method leverages the geometric uncertainty and is applicable in indoor scenarios predominated by diffuse illumination. Experiments on simulation and real data sets demonstrate the efficiency of our proposed method. The results further show that combining the CNN-based framework with geometric constraint achieves better accuracy when compared with CNN-only methods, especially when the training data size is small.

  Energy and Delay aware Physical Collision Avoidance in Unmanned Aerial Vehicles

• Authors: S. Ouahouah, J. Prados, T. Taleb, C. Benzaid
• Subjects: Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
• Arxiv link: https://arxiv.org/abs/2201.01414
• Pdf link: https://arxiv.org/pdf/2201.01414
• Abstract Several solutions have been proposed in the literature to address the Unmanned Aerial Vehicles (UAVs) collision avoidance problem. Most of these solutions consider that the ground controller system (GCS) determines the path of a UAV before starting a particular mission at hand. Furthermore, these solutions expect the occurrence of collisions based only on the GPS localization of UAVs as well as via object-detecting sensors placed on board UAVs. The sensors' sensitivity to environmental disturbances and the UAVs' influence on their accuracy impact negatively the efficiency of these solutions. In this vein, this paper proposes a new energy and delay-aware physical collision avoidance solution for UAVs. The solution is dubbed EDC-UAV. The primary goal of EDC-UAV is to build inflight safe UAVs trajectories while minimizing the energy consumption and response time. We assume that each UAV is equipped with a global positioning system (GPS) sensor to identify its position. Moreover, we take into account the margin error of the GPS to provide the position of a given UAV. The location of each UAV is gathered by a cluster head, which is the UAV that has either the highest autonomy or the greatest computational capacity. The cluster head runs the EDC-UAV algorithm to control the rest of the UAVs, thus guaranteeing a collision-free mission and minimizing the energy consumption to achieve different purposes. The proper operation of our solution is validated through simulations. The obtained results demonstrate the efficiency of EDC-UAV in achieving its design goals.

  Multi-layer VI-GNSS Global Positioning Framework with Numerical Solution aided MAP Initialization

• Authors: Bing Han, Zhongyang Xiao, Shuai Huang, Tao Zhang
• Subjects: Robotics (cs.RO)
• Arxiv link: https://arxiv.org/abs/2201.01561
• Pdf link: https://arxiv.org/pdf/2201.01561
• Abstract Motivated by the goal of achieving long-term drift-free camera pose estimation in complex scenarios, we propose a global positioning framework fusing visual, inertial and Global Navigation Satellite System (GNSS) measurements in multiple layers. Different from previous loosely- and tightly- coupled methods, the proposed multi-layer fusion allows us to delicately correct the drift of visual odometry and keep reliable positioning while GNSS degrades. In particular, local motion estimation is conducted in the inner-layer, solving the problem of scale drift and inaccurate bias estimation in visual odometry by fusing the velocity of GNSS, pre-integration of Inertial Measurement Unit (IMU) and camera measurement in a tightly-coupled way. The global localization is achieved in the outer-layer, where the local motion is further fused with GNSS position and course in a long-term period in a loosely-coupled way. Furthermore, a dedicated initialization method is proposed to guarantee fast and accurate estimation for all state variables and parameters. We give exhaustive tests of the proposed framework on indoor and outdoor public datasets. The mean localization error is reduced up to 63%, with a promotion of 69% in initialization accuracy compared with state-of-the-art works. We have applied the algorithm to Augmented Reality (AR) navigation, crowd sourcing high-precision map update and other large-scale applications.

  Real-time Interface Control with Motion Gesture Recognition based on Non-contact Capacitive Sensing

• Authors: Hunmin Lee, Jaya Krishna Mandivarapu, Nahom Ogbazghi, Yingshu Li
• Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
• Arxiv link: https://arxiv.org/abs/2201.01755
• Pdf link: https://arxiv.org/pdf/2201.01755
• Abstract Capacitive sensing is a prominent technology that is cost-effective and low power consuming with fast recognition speed compared to existing sensing systems. On account of these advantages, Capacitive sensing has been widely studied and commercialized in the domains of touch sensing, localization, existence detection, and contact sensing interface application such as human-computer interaction. However, as a non-contact proximity sensing scheme is easily affected by the disturbance of peripheral objects or surroundings, it requires considerable sensitive data processing than contact sensing, limiting the use of its further utilization. In this paper, we propose a real-time interface control framework based on non-contact hand motion gesture recognition through processing the raw signals, detecting the electric field disturbance triggered by the hand gesture movements near the capacitive sensor using adaptive threshold, and extracting the significant signal frame, covering the authentic signal intervals with 98.8% detection rate and 98.4% frame correction rate. Through the GRU model trained with the extracted signal frame, we classify the 10 hand motion gesture types with 98.79% accuracy. The framework transmits the classification result and maneuvers the interface of the foreground process depending on the input. This study suggests the feasibility of intuitive interface technology, which accommodates the flexible interaction between human to machine similar to Natural User Interface, and uplifts the possibility of commercialization based on measuring the electric field disturbance through non-contact proximity sensing which is state-of-the-art sensing technology.