However, the frequency-modulated continuous wave modulation of radar signals makes it more sensitive to vehicles mobility than optical sensors. 4071-4079. Electric Sense Based Pose Estimation and Localization for Small Underwater Robots, Accurate pose estimation and localization technology is always a challenge for small underwater robots, since the under-water lighting conditions could limit the use of cameras while the cramped environments restrict the use of sonars. The proposed GAINS can accurately model the raw measurement uncertainties by canceling the atmospheric effects (e.g., ionospheric and tropospheric delays) which requires no prior model information. [37] BLOESCH M, BURRI M, OMARI S, et al. The evaluation is performed on the KITTI dataset and a novel synthetic dataset including low-overlapping point clouds with displacements of up to 30m. The virtual-to-real domain gap is bridged by using an adversarial training strategy to map images from both domains into a shared feature space. 910 VIO The first algorithm combines right invariant error states with first-estimates Jacobian (FEJ) technique, by decoupling the features from the Lie group representation and utilizing FEJ for consistent estimation. [108] LIU Z, ZHANG F. BALM[EB/OL]. UV-SLAM: Unconstrained Line-Based SLAM Using Vanishing Points for Structural Mapping. Secondly, LaneMatch utilizes a spatio-temporal integration of a particle filter and a factor graph to resolve lane-matching ambiguities. , Xueying Qin, We validate the effectiveness of our approach in both monocular and stereo modes on the public KITTI dataset. In addition, delayed marginalization enables us to inject IMU information into already marginalized states. In this paper, we propose the multi-agent visual semantic navigation, in which multiple agents collaborate with others to find multiple target objects. Are you sure you want to create this branch? In order to meet these challenges, we propose an accurate and robust extrinsic calibration method for long baseline multi-LiDAR systems, named LB-L2L-Calib (Large Baseline LiDAR to LiDAR extrinsic Calibration). The proposed method improves the localization and object global mapping accuracy by probabilistically accounting for inertial readings and object pose uncertainties at multiple views. IEEE, 2019: 3556-3562. DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions. IEEE Inspired by such passive electric sense behavior in fish, this letter presents an electro-localization scheme based on passive electric sense for short-distance accurate pose estimation and localization of small underwater robots. Li J, Bao H, Zhang G. Rapid and Robust Monocular Visual-Inertial Initialization with Gravity Estimation via Vertical Edges[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM pp. This makes our system highly efficient in that minimal set of correspondences is needed and highly accurate as the number of outliers is low. Our novel clinical dataset MITI is comparable to state-of-the-art evaluation datasets contains calibration and synchronization and is available at https://mediatum.ub.tum.de/1621941. IEEE Robotics and Automation Letters, 2021, 6(2): 3184-3191. [24] WANG R , SCHWRER M , CREMERS D . [28] MOURIKIS A I , ROUMELIOTIS S I . 2019. https://gitlab.com/VladyslavUsenko/ basalt. IEEE Robotics and Automation Letters, 2021, 6(2): 1004-1011. lidarLO LiDAR While end-to-end solutions - which learn a global descriptor from input point clouds - have demonstrated promising results, such approaches are limited in their ability to enforce desirable properties at the local feature level. FEJ2: A Consistent Visual-Inertial State Estimator Design, Continuous-Time Spline Visual-Inertial Odometry. For current mobile phone-based AR, this is usually only a monocular camera. IEEE Transactions on Image Processing, 26(12): 5966 - 5979, Best Paper Finalist Award on Safety, Security, and Rescue Robotics in memory of Motohiro Kisoi, IEEE Then, optimized with the corresponding relationship, the map accuracy is significantly improved. SLAM and rotation averaging are typically formalized as large-scale nonconvex point estimation problems, with many bad local minima that can entrap the smooth optimization methods typically applied to solve them; the performance of standard SLAM and RA algorithms thus crucially depends upon the quality of the estimates used to initialize this local search. Additionally, our framework includes a two-stage global and local optimization framework for distributed multi-robot SLAM which provides stable localization results that are resilient to the unknown initial conditions that typify the search for inter-robot loop closures. Continuous-time pose representation makes it possible to address many VIO challenges, e.g., rolling shutter distortion and sensors that may lack synchronization. 2020. https://github.com/TixiaoShan/LIO-SAM. By maintaining end-to-end differentiability a neural network is used to mask scans and trained by supervising pose prediction directly. Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task Uncertainty Estimation. Within our approach, these structural constraints are initially used to estimate accurately the 3D position of the extracted lines. 2122 3D The experimental results show that our algorithm outperforms the state-of-the-art direct VO algorithms. LBA, : In this way, the integration of asynchronous or continuous high-rate streams of sensor data does not require tailored and highly-engineered algorithms, enabling the fusion of multiple sensor modalities in an intuitive fashion. LSD-SLAM: Large-scale direct monocular SLAM[C]// European Conference on Computer Vision. 4071-4079. We propose a novel method to tackle the visual-inertial SLAM localization problem for constrained camera movements. Li J, Bao H, Zhang G. Rapid and Robust Monocular Visual-Inertial Initialization with Gravity Estimation via Vertical Edges[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Furthermore, we designed a stable line matching method based on frame-to-frame (2d-2d) and map-to-frame (3d-2d) strategies which can significantly improve the trajectory accuracy of the system. [53] GENEVA P, ECKENHOFF K, LEE W, et al. 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We develop a hierarchical decision framework based on semantic mapping, scene prior knowledge, and communication mechanism to solve this task. This paper introduces the concept of optimizing target shape to remove pose ambiguity for LiDAR point clouds. Deep learning has recently been introduced to address this issue by leveraging stereo sequences or ground-truth motions in the training dataset. In this paper, we propose a stereo visual SLAM with a robust quadric landmark representation method. [95] QIN C, YE H, PRANATA C E, et al. 7,16 Interval-Based Visual-Inertial LiDAR SLAM with Anchoring Poses. We present a distributed, graph-based SLAM framework for incrementally building scene graphs based on two novel components. Li J, Bao H, Zhang G. Rapid and Robust Monocular Visual-Inertial Initialization with Gravity Estimation via Vertical Edges[C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). [64] GLVEZ-LPEZ D, TARDOS J D. DBoW2[EB/OL]. [14] NeuralRecon, 2021 4 15 In this work, we present an information-based framework for online extrinsic calibration of multi-camera systems. 1974-1982. Experiments on two large-scale public benchmarks (KITTI and MulRan) show that our method achieves mean F1max scores of 0.939 and 0.968 on KITTI and MulRan respectively, achieving state-of-the-art performance while operating in near real-time. 2018 11 SLAM Cube SLAM . [88] KOIDE K, MIURA J, MENEGATTI E. A portable 3d lidar-based system for long-term and wide-area people behavior measurement[J]. This increases accuracy and achieves a more compact factor graph representation. Existing LiDAR-only odometry algorithms generally ignore this distortion or compensate by linearly interpolating the estimated relative motion between scans. Map-Based Visual-Inertial Localization: A Numerical Study, VIOEKFSKFMSCKF-based. IEEE Transactions on Robotics, 2018, 34(4): 1004-1020. We present a novel approach to improve 6 degree-of-freedom state propagation for unmanned aerial vehicles in a classical filter through pre-processing of high-speed inertial data with AI algorithms. The dataset comprises 36 sequences (about 1TB) captured in diverse scenarios including both indoor and outdoor environments. arXiv 2018[J]. [118] ZHANG J, SINGH S. Laservisualinertial odometry and mapping with high robustness and low drift[J]. We propose a new radar-based metric localization framework, termed DC-Loc, which can obtain more accurate location estimation by restoring the Doppler distortion. Incremental, Consistent and Efficient Bundle Adjustment for Visual-Inertial SLAM pp. Masking by Moving (MbyM), provides robust and accurate radar odometry measurements through an exhaustive correlative search across discretised pose candidates. Our work addresses this particular issue and shows by exploiting an interesting concept of sparse 3D models that we can exploit discriminatory environment parts and avoid useless image regions for the sake of a single image localization. [69] FORSTER C, CARLONE L, DELLAERT F, et al. Please IEEE Transactions on Robotics, 24(5):932945, 2008. Decoupled Right Invariant Error States for Consistent Visual-Inertial Navigation, featuresIEKF. 22 7 1 Our approach is validated on both public and self-collected datasets captured under various conditions. Compared to methods that use prior LiDAR maps, our method presents two main advantages: (1) vehicle localization is not limited to only places with previously acquired LiDAR maps, and (2) our method is comparable to LiDAR map-based methods, and especially out- performs the other methods with respect to the top one candidate at KITTI dataset sequence 00. 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Yu, BANANA: when Behavior ANAlysis meets social Network Alignment, Fuxin Ren, Zhongbao Zhang, Jiawei Zhang, Sen Su, Li Sun, Guozhen Zhu, Congying Guo, Proximal Gradient Algorithm with Momentum and Flexible Parameter Restart for Nonconvex Optimization, Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, Vahid Tarokh, Bilinear Graph Neural Network with Neighbor Interactions, Hongmin Zhu, Fuli Feng, Xiangnan He, Xiang Wang, Yan Li, Kai Zheng, Yongdong Zhang, Unsatisfiability Proofs for Weight 16 Codewords in Lam's Problem, Curtis Bright, Kevin K.H. This effectively distributes expensive operations across time, resulting in a very fast and lightweight system with a much higher throughput and lower latency. [35] LEUTENEGGER S, Forster A, Furgale P, et al. This paper develops open-source, modular, and readily available LiDAR-based lifelong mapping for urban sites. However, this dense search creates a significant computational bottleneck which hinders real-time performance when high-end GPUs are not available. Our system extends direct sparse odometry by using a spherical camera model to process equirectangular images without rectification to attain omnidirectional perception. M2DGR: A Multi-Sensor and Multi-Scenario SLAM Dataset for Ground Robots. This image from the MonoSLAM algorithm by I. D. Reid et al. The experimental results va, HD Ground - a Database for Ground Texture Based Localization. 3D3DvSLAMSLAMVR/AR3D~, CV_LAB/++, folders/110Hko3zPcDmY0_bnZdXxJXJKe6wr3t10?usp=sharing, 2.3D(+/+), 8.SLAM(cartographer+LOAM +LIO-SAM). [116] ZUO X, GENEVA P, YANG Y, et al. The ground landmark observation constraints are fused into the pose graph optimization framework to improve the LO performance. A new pseudo-anchor change algorithm is also proposed to maintain the features in the state vector longer than the window span. Constrained Visual-Inertial Localization with Application and Benchmark in Laparoscopic Surgery. ORB-SLAM3 , 2020 6 27 [12] KLEIN G, MURRAY D W. PTAM-GPL[EB/OL]. Compared to PTAM, RDSLAM not only can robustly work in dynamic environments, but also can handle a larger scale scene (the number of the reconstructed 3D points can be tens of thousands). 2020/04/19 [] 2021/07/051. The mobile robot was equipped with a 2D laser scanner, a monocular and stereo camera. 2064-2073. In addition, memory access accounts for a signficant portion of the computing energy. [29] LI M , MOURIKIS A I . This paper presents a novel place recognition approach to autonomous vehicles by using low-cost, single-chip automotive radars. All the implementations and datasets are available at https://github.com/UMich- BipedLab/global_pose_estimation_for_optimal_shape. Our system achieves obvious advantages in accuracy and efficiency. In this paper, we propose a novel direct visual odometry algorithm to take the advantage of a 360-degree camera for robust localization and mapping. This paper introduces an efficient direct visual odometry (VO) algorithm using points and lines. LS-ACTS is a robust and efficient structure-from-motion system which can recover camera motion and 3D scene structure from large videos/sequences datasets. IEEE, 2011: 1-4. 2020. https://github.com/wh2007 20041/iscloam. For the benefit of the research community, we make the dataset and tools public. Oregon State University Keywords: Robotics and Automation in Agriculture and Forestry , Grasping , Data Sets for Robot Learning Abstract: Apple picking is a challenging manipulation task, but it is difficult to test solutions due to the limited window of time that apples are in season. The accuracy of such an approach is heavily dependent on the quality of the extracted scene-level representation. Large-scale direct SLAM with stereo cameras[C]// 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Our method integrates the improvements typical for hand-crafted descriptors (like ScanContext) with the most efficient 3D sparse convolutions (MinkLoc3D). (2) Make progress towards creating truly intelligent machines. Our approach relies on robust ceiling and ground plane detection, which solves part of the pose and supports the segmentation of vertical structure elements such as walls and pillars. Paper ID: Paper Title: Authors: 8: Learning Uncoupled-Modulation CVAE for 3D Action-Conditioned Human Motion Synthesis: Chongyang Zhong (Institute of Computing Technology, Chinese Academy of Sciences)*; Lei Hu (Institute of Computing Technology, Chinese Academy of Sciences ); Zihao Zhang (Institute of Computing Technology, Chinese Academy of Sciences); Shihong Xia (institute Our work will be publicly available, Towards Scale Consistent Monocular Visual Odometry by Learning from the Virtual World. , liquan0915: Our open source implementation is available at https://github.com/versatran01/llol. Accurate camera pose estimation is a fundamental requirement for numerous applications, such as autonomous driving, mobile robotics, and augmented reality. At MTank, we work towards two goals: (1) Model and distil knowledge within AI. Peng Wei, Guoliang Hua, Weibo Huang, Fanyang Meng, Hong Liu An Augmented Index-based Approach. Specifically, we first find the association between the street map and the reconstructed point cloud structure by a novel graph-based geolocalization method. In this work we present the first initialization methods equipped with explicit performance guarantees that are adapted to the pose-graph simultaneous localization and mapping (SLAM) and rotation averaging (RA) problems. 2014. https:// http://github.com/uzh-rpg/rpg svo. [25] ENGEL J , KOLTUN V , CREMERS D. DSO: Direct Sparse Odometry[EB/OL]. Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang. 2019. https://github.com/HKUST-Aerial-Robotics/VINS-Fusion. Simultaneous Localization and Mapping is now widely adopted by many applications, and researchers have produced very dense literature on this topic. LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition. [27] ZUBIZARRETAJON, AGUINAGAIKER, MARTINEZ M . IEEE, 2017. At inference time, the relative pose transformation is obtained by robustly fitting the correspondences through sample consensus. In this paper, we presented HiPE: a novel hierarchical algorithm for pose graph initialization. LS-ACTS: Large-Scale Automatic Camera Tracking System. Multi-beam LiDAR sensors are increasingly used in robotics, particularly with autonomous cars for localization and perception tasks, both relying on the ability to build a precise map of the environment. LLOL: Low-Latency Odometry for Spinning Lidars. Translating Images into Maps (WINNER)code LTSR: Long-Term Semantic Relocalization Based on HD Map for Autonomous Vehicles. Jundan Luo, Zhaoyang Huang, Yijin Li, Xiaowei Zhou. Besides, for heading angle estimation between point clouds with different distributions, we implement this heading angle estimator as a differentiable module to train a feature extraction network end-to-end. Xingbin Yang, Liyang Zhou, Hanqing Jiang, Zhongliang Tang, Yuanbo Wang, Hujun Bao. With the advent of smart devices, embedding cameras, inertial measurement units, visual SLAM (vSLAM), and visual-inertial SLAM (viSLAM) are enabling novel general public applications. Jiaming Sun, Zihao Wang, Siyu Zhang, Xingyi He, Hongcheng Zhao. 20 3 We compare the performance of our system with state-of-the-art point cloud-based methods, LOAM, LeGO-LOAM, A-LOAM, LeGO-LOAM-BOR, LIO-SAM and HDL, and show that the proposed system achieves equal or better accuracy and can easily handle even cases without loops. The open-source implementation is available at: https://github.com/csiro-robotics/LoGG3D-Net. This property can significantly reduce the number of variables to speed up the optimization, and can make the collinear constraint exactly satisfied. 1D-LRF Aided Visual-Inertial Odometry for High-Altitude MAV Flight. Thus, SPGF enables real-time mapping of large 3D environments on energy-constrained rob, Multi-Agent Embodied Visual Semantic Navigation with Scene Prior Knowledge. Lic-fusion: Lidar-inertial-camera odometry[J]. July 2018: We have released the source code of ENFT-SfM, SegmentBA, EIBA and ICE-BA. It can run real-time on a mobile device and outperform state-of-the-art systems (e.g. He is a core member of, Robust Keyframe-based Monocular SLAM for AR, Large-Scale Automatic Camera Tracking System, Robust Dynamic Simultaneous Localization and Mapping. [100] SHAN T. LIO-SAM[EB/OL]. An Exploratory Study on Semantic Parsing in Context, Qian Liu, Bei Chen, Jiaqi Guo, Jian-Guang Lou, Bin Zhou, Dongmei Zhang, Lexical-Constraint-Aware Neural Machine Translation via Data Augmentation, Guanhua Chen, Yun Chen, Yong Wang, Victor O.K. A general optimization-based framework for local odometry estimation with multiple sensors[J]. In this work, we focus on the parameterization of frequently used geometric primitives including points, lines, planes, ellipsoids, cylinders, and cones. However, we found typical LO results are prone to drift upwards along the vertical direction in underground parking lots, leading to poor mapping results. Lu and Milios (1997) proposed a basic graph structured model for SLAM called Graph-SLAM to find the robot pose in an area based on the robot motion and observation data.As shown in Fig. Theoretical derivation and analysis are detailed first, and then, the experimental results are presented to support the proposed theory. Learning Illumination for Unconstrained Mobile Mixed Reality pp. Dual Skipping Networks pp. [23] HIDENOBU M , LUKAS V S , VLADYSLAV U , et al. weixin_47034532: OKVIS: Open keyframe-based visual-inertial SLAM (ROS version) [EB/OL]. [10] VDO-SLAM Zhichao Ye, Chong Bao, Xinyang Liu, Hujun Bao, Zhaopeng Cui. [79] ZHANG J, SINGH S. LOAM[EB/OL]. SLAM . To establish the connection between global measurements and local states, a coarse-to-fine initialization procedure is proposed to efficiently calibrate the transformation online and initialize GNSS states from only a short window of measurements. 1974-1982. 2016. https://github.com/erik-nelson/blam. (3) A extrinsic parameter regression scheme is introduced. Specifically, during the MSCKF visual measurement update, we deliberately constrain the depth of those SLAM features co-planar with the single LRF measuring point. With only relying on single image at inference, it outweighs in terms of accuracy methods that exploit pose priors and/or reference 3D models while being much faster. IEEE, 2014. This paper addresses the problem of visual-inertial odometry (VIO) with a downward facing monocular camera when a micro aerial vehicle (MAV) flying at high altitude (over 100 meters). Besides camera motion, it also can recover accurate and dense depth maps now. In this paper we propose a novel long-term semantic relocalization algorithm based on HD map and semantic features which are compact in representation. Drift-free. This allows us to later readvance this delayed graph, yielding an updated marginalization prior with new and consistent linearization points. Guofeng Zhang, Jiaya Jia, Tien-Tsin Wong, and Hujun Bao. Accurate camera pose estimation is a fundamental requirement for numerous applications, such as autonomous driving, mobile robotics, and augmented reality. However, discrete-time SLAM needs tailored algorithms and simplifying assumptions when high-rate and/or asynchronous measurements, coming from different sensors, are present in the estimation process. This paper presents a real-time globally consistent mapping framework based on LiDAR-IMU tight coupling. Real-time loop closure in 2D LIDAR SLAM[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). LaneMatch: A Practical Real-Time Localization Method Via Lane-Matching. However, when there are abrupt and nonlinear motion changes, the linear interpolation method poorly compensates for the distortions, which can cause significant drift in motion estimates. The evaluation results on the Newer College dataset and KAIST urban dataset show that the proposed framework enables accurate and robust localization and mapping in challenging environments. Tightly-coupled Monocular Visual-odometric SLAM using Wheels and a MEMS Gyroscope[J]. (2) The proposed method gives an explicit fusion formalism on SE(3) and SE(2), which covers the most use cases in the field of robotics. MinkLoc3D-SI: 3D LiDAR Place Recognition with Sparse Convolutions, Spherical Coordinates, and Intensity. The method allows us to restrict the solution set of the robot poses and the position of the landmarks to the set that is consistent with the measurements. IEEE Robotics and Automation Letters, 2019, 5(2): 422-429. 7 SLAM 8910 However, most of the existing models are only effective for single-agent navigation, and a single agent has low efficiency and poor fault tolerance when conducting more complicated tasks. Jinyu Li, Bangbang Yang, Danpeng Chen, Nan Wang. Using publicly accessible maps, we propose a novel vehicle localization method that can be applied without using prior light detection and ranging (LiDAR) maps. State-of-the-art quadric-based SLAM algorithms always face observation-related problems and are sensitive to observation noise, which limits their application in outdoor scenes. [22] ENGEL J , KOLTUN V , CREMERS D . It is a meaningful task inspiring a surge of relevant research. The extensive experiments indicate that our system achieves start of the art results. ) Visual odometry revisited: What should be learnt? Uncontrolled camera. In this paper, we introduce a novel online removal framework for highly dynamic urban environments. 8-11 SLAM This paper focuses on using structural regularities without any constraints, such as the Manhattan world assumption. IEEE Transactions on robotics, 2016, 32(6):1309-1332. MAPmaximum-a-posteriori In settings where image quality is low and disturbances are frequent, the residuals reduce the complexity of the problem and make localization feasible. Murthy Jatavallabhula K, Iyer G, Paull L. Kasyanov A, Engelmann F, Stckler J, et al. Haomin Liu, Mingxuan Jiang, Zhuang Zhang, Xiaopeng Huang, Linsheng Zhao, Meng Hang, Youji Feng, Hujun Bao. It utilizes lane matching to obtain an AVs lane occupancy and current pose estimation. [52] GENEVA P, ECKENHOFF K, LEE W, et al. [10] DynaSLAM Conference on Computer Vision and Pattern Recognition (CVPR),