scholarly journals Monocular Visual-Inertial Navigation for Dynamic Environment

2021 ◽  
Vol 13 (9) ◽  
pp. 1610
Author(s):  
Dong Fu ◽  
Hao Xia ◽  
Yanyou Qiao
Keyword(s):  
Moving Objects ◽  
Dynamic Environment ◽  
Image Feature ◽  
Measurement Unit ◽  
Feature Points ◽  
Location Accuracy ◽  
Elimination Algorithm ◽  
Localization And Mapping ◽  
Stationary Objects ◽  
Public Datasets

Simultaneous localization and mapping (SLAM) systems have been generally limited to static environments. Moving objects considerably reduce the location accuracy of SLAM systems, rendering them unsuitable for several applications. Using a combined vision camera and inertial measurement unit (IMU) to separate moving and static objects in dynamic scenes, we improve the location accuracy and adaptability of SLAM systems in these scenes. We develop a moving object-matched feature points elimination algorithm that uses IMU data to eliminate matches on moving objects but retains them on stationary objects. Moreover, we develop a second algorithm to validate the IMU data to avoid erroneous data from influencing image feature points matching. We test the new algorithms with public datasets and in a real-world experiment. In terms of the root mean square error of the location absolute pose error, the proposed method exhibited higher positioning accuracy for the public datasets than the traditional algorithms. Compared with the closed-loop errors obtained by OKVIS-mono and VINS-mono, those obtained in the practical experiment were lower by 50.17% and 56.91%, respectively. Thus, the proposed method eliminates the matching points on moving objects effectively and achieves feature point matching results that are realistic.

scholarly journals RESEARCH ON SEMANTIC-ASSISTED SLAM IN COMPLEX DYNAMIC INDOOR ENVIRONMENT

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 353-359
Author(s):  
C. Li ◽  
Z. Kang ◽  
J. Yang ◽  
F. Li ◽  
Y. Wang
Keyword(s):  
Indoor Environment ◽  
Moving Objects ◽  
Solution Process ◽  
Dynamic Environments ◽  
Satellite System ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Feature Points ◽  
Localization And Mapping ◽  
Object Area

Abstract. Visual Simultaneous Localization and Mapping (SLAM) systems have been widely investigated in response to requirements, since the traditional positioning technology, such as Global Navigation Satellite System (GNSS), cannot accomplish tasks in restricted environments. However, traditional SLAM methods which are mostly based on point feature tracking, usually fail in harsh environments. Previous works have proven that insufficient feature points caused by missing textures, feature mismatches caused by too fast camera movements, and abrupt illumination changes will eventually cause state estimation to fail. And meanwhile, pedestrians are unavoidable, which introduces fake feature associations, thus violating the strict assumption that the unknown environment is static in SLAM. In order to ensure how our system copes with the huge challenges brought by these factors in a complex indoor environment, this paper proposes a semantic-assisted Visual Inertial Odometer (VIO) system towards low-textured scenes and highly dynamic environments. The trained U-net will be used to detect moving objects. Then all feature points in the dynamic object area need to be eliminated, so as to avoid moving objects to participate in the pose solution process and improve robustness in dynamic environments. Finally, the constraints of inertial measurement unit (IMU) are added for low-textured environments. To evaluate the performance of the proposed method, experiments were conducted on the EuRoC and TUM public dataset, and the results demonstrate that the performance of our approach is robust in complex indoor environments.

scholarly journals A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information

2021 ◽  
Vol 10 (10) ◽  
pp. 673
Author(s):  
Sheng Miao ◽  
Xiaoxiong Liu ◽  
Dazheng Wei ◽  
Changze Li
Keyword(s):  
Moving Objects ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Visual Localization ◽  
Epipolar Constraint ◽  
Trajectory Error ◽  
Localization And Mapping ◽  
Dynamic Objects ◽  
Public Datasets ◽  
Localization Approach

A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios.

Multi-Sensor Fusion in Dynamic Environment using Evidential Grid Mapping

2020 ◽  
Vol 2020 (16) ◽  
pp. 255-1-255-7
Author(s):  
G. M. Dilshan P. Godaliyadda ◽  
Vijay Pothukuchi ◽  
JuneChul Roh
Keyword(s):  
Autonomous Navigation ◽  
Moving Objects ◽  
Dynamic Environment ◽  
Lidar Measurement ◽  
Seamless Integration ◽  
Dempster Shafer Theory ◽  
Sensor Model ◽  
Grid Mapping ◽  
Stationary Objects ◽  
Shafer Theory

Grid mapping is widely used to represent the environment surrounding a car or a robot for autonomous navigation. This paper describes an algorithm for evidential occupancy grid (OG) mapping that fuses measurements from different sensors, based on the Dempster-Shafer theory, and is intended for scenes with stationary and moving (dynamic) objects. Conventional OGmapping algorithms tend to struggle in the presence of moving objects because they do not explicitly distinguish between moving and stationary objects. In contrast, evidential OG mapping allows for dynamic and ambiguous states (e.g. a LIDAR measurement: cannot differentiate between moving and stationary objects) that are more aligned with measurements made by sensors. In this paper, we present a framework for fusing measurements as they are received from disparate sensors (e.g. radar, camera and LIDAR) using evidential grid mapping. With this approach, we can form a live map of the environment, and also alleviate the problem of having to synchronize sensors in time. We also designed a new inverse sensor model for radar that allows us to extract more information from object level measurements, by incorporating knowledge of the sensor’s characteristics. We have implemented our algorithm in the OpenVX framework to enable seamless integration into embedded platforms. Test results show compelling performance especially in the presence of moving objects.

scholarly journals SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3699 ◽  
Author(s):  
Masoud S. Bahraini ◽  
Ahmad B. Rad ◽  
Mohammad Bozorg
Keyword(s):  
Target Tracking ◽  
Real Time ◽  
Dynamic Environment ◽  
Dynamic Environments ◽  
Depth Information ◽  
Localization And Mapping ◽  
Vision Sensors ◽  
Multi Target Tracking ◽  
Stationary Objects ◽  
Feature Based

The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications.

A SLAM-Based Mobile Augmented Reality Tracking Registration Algorithm

2019 ◽  
Vol 34 (01) ◽  
pp. 2054005
Author(s):  
Jia Liu ◽  
Yulei Xie ◽  
Shuang Gu ◽  
Xu Chen
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Measurement Unit ◽  
Camera Motion ◽  
Mobile Augmented Reality ◽  
Feature Points ◽  
Registration Algorithm ◽  
Localization And Mapping ◽  
Camera Localization ◽  
Slam Algorithm

This paper proposes a simultaneous localization and mapping (SLAM)-based markerless mobile-end tracking registration algorithm to address the problem of virtual image drift caused by fast camera motion in mobile-augmented reality (AR). The proposed algorithm combines the AGAST-FREAK-SLAM algorithm with inertial measurement unit (IMU) data to construct a scene map and localize the camera’s pose. The extracted feature points are matched with feature points in a map library for real-time camera localization and precise registration of virtual objects. Experimental results show that the proposed method can track feature points in real time, accurately construct scene maps, and locate cameras; moreover, it improves upon the tracking and registration robustness of earlier algorithms.

scholarly journals FSD-BRIEF: A Distorted BRIEF Descriptor for Fisheye Image Based on Spherical Perspective Model

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1839
Author(s):  
Yutong Zhang ◽  
Jianmei Song ◽  
Yan Ding ◽  
Yating Yuan ◽  
Hua-Liang Wei
Keyword(s):  
Feature Matching ◽  
Field Of View ◽  
Image Feature ◽  
Feature Points ◽  
Matching Process ◽  
Feature Descriptors ◽  
Coordinate Mapping ◽  
Matching Performance ◽  
Perspective Model ◽  
Attitude Matrix

Fisheye images with a far larger Field of View (FOV) have severe radial distortion, with the result that the associated image feature matching process cannot achieve the best performance if the traditional feature descriptors are used. To address this challenge, this paper reports a novel distorted Binary Robust Independent Elementary Feature (BRIEF) descriptor for fisheye images based on a spherical perspective model. Firstly, the 3D gray centroid of feature points is designed, and the position and direction of the feature points on the spherical image are described by a constructed feature point attitude matrix. Then, based on the attitude matrix of feature points, the coordinate mapping relationship between the BRIEF descriptor template and the fisheye image is established to realize the computation associated with the distorted BRIEF descriptor. Four experiments are provided to test and verify the invariance and matching performance of the proposed descriptor for a fisheye image. The experimental results show that the proposed descriptor works well for distortion invariance and can significantly improve the matching performance in fisheye images.

scholarly journals Optimization-Based Online Initialization and Calibration of Monocular Visual-Inertial Odometry Considering Spatial-Temporal Constraints

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2673
Author(s):  
Weibo Huang ◽  
Weiwei Wan ◽  
Hong Liu
Keyword(s):  
Measurement Data ◽  
Calibration Method ◽  
Bundle Adjustment ◽  
Measurement Unit ◽  
Temporal Parameters ◽  
Work Routine ◽  
Initial States ◽  
Time Offset ◽  
Public Datasets ◽  
Metric Scale

The online system state initialization and simultaneous spatial-temporal calibration are critical for monocular Visual-Inertial Odometry (VIO) since these parameters are either not well provided or even unknown. Although impressive performance has been achieved, most of the existing methods are designed for filter-based VIOs. For the optimization-based VIOs, there is not much online spatial-temporal calibration method in the literature. In this paper, we propose an optimization-based online initialization and spatial-temporal calibration method for VIO. The method does not need any prior knowledge about spatial and temporal configurations. It estimates the initial states of metric-scale, velocity, gravity, Inertial Measurement Unit (IMU) biases, and calibrates the coordinate transformation and time offsets between the camera and IMU sensors. The work routine of the method is as follows. First, it uses a time offset model and two short-term motion interpolation algorithms to align and interpolate the camera and IMU measurement data. Then, the aligned and interpolated results are sent to an incremental estimator to estimate the initial states and the spatial–temporal parameters. After that, a bundle adjustment is additionally included to improve the accuracy of the estimated results. Experiments using both synthetic and public datasets are performed to examine the performance of the proposed method. The results show that both the initial states and the spatial-temporal parameters can be well estimated. The method outperforms other contemporary methods used for comparison.

scholarly journals CDL-GAN: Contrastive Distance Learning Generative Adversarial Network for Image Generation

2021 ◽  
Vol 11 (4) ◽  
pp. 1380
Author(s):  
Yingbo Zhou ◽  
Pengcheng Zhao ◽  
Weiqin Tong ◽  
Yongxin Zhu
Keyword(s):  
Distance Learning ◽  
Feature Learning ◽  
Image Synthesis ◽  
Image Feature ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
Generative Adversarial Network ◽  
Feature Representations ◽  
Adversarial Network ◽  
Public Datasets

While Generative Adversarial Networks (GANs) have shown promising performance in image generation, they suffer from numerous issues such as mode collapse and training instability. To stabilize GAN training and improve image synthesis quality with diversity, we propose a simple yet effective approach as Contrastive Distance Learning GAN (CDL-GAN) in this paper. Specifically, we add Consistent Contrastive Distance (CoCD) and Characteristic Contrastive Distance (ChCD) into a principled framework to improve GAN performance. The CoCD explicitly maximizes the ratio of the distance between generated images and the increment between noise vectors to strengthen image feature learning for the generator. The ChCD measures the sampling distance of the encoded images in Euler space to boost feature representations for the discriminator. We model the framework by employing Siamese Network as a module into GANs without any modification on the backbone. Both qualitative and quantitative experiments conducted on three public datasets demonstrate the effectiveness of our method.

scholarly journals Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 230
Author(s):  
Xiangwei Dang ◽  
Zheng Rong ◽  
Xingdong Liang
Keyword(s):  
State Estimation ◽  
Autonomous Navigation ◽  
Moving Objects ◽  
Physical Simulation ◽  
Point Clouds ◽  
Dynamic Environments ◽  
Accurate Localization ◽  
Novel Approach ◽  
Localization And Mapping ◽  
Real World Datasets

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

scholarly journals An Optimized Tightly-Coupled VIO Design on the Basis of the Fused Point and Line Features for Patrol Robot Navigation

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2004 ◽  
Author(s):  
Linlin Xia ◽  
Qingyu Meng ◽  
Deru Chi ◽  
Bo Meng ◽  
Hanrui Yang
Keyword(s):  
Global Positioning System ◽  
Inertial Measurement Unit ◽  
Feature Matching ◽  
Robot Navigation ◽  
Measurement Unit ◽  
State Estimator ◽  
Localization And Mapping ◽  
Global Positioning ◽  
Tightly Coupled ◽  
Association State

The development and maturation of simultaneous localization and mapping (SLAM) in robotics opens the door to the application of a visual inertial odometry (VIO) to the robot navigation system. For a patrol robot with no available Global Positioning System (GPS) support, the embedded VIO components, which are generally composed of an Inertial Measurement Unit (IMU) and a camera, fuse the inertial recursion with SLAM calculation tasks, and enable the robot to estimate its location within a map. The highlights of the optimized VIO design lie in the simplified VIO initialization strategy as well as the fused point and line feature-matching based method for efficient pose estimates in the front-end. With a tightly-coupled VIO anatomy, the system state is explicitly expressed in a vector and further estimated by the state estimator. The consequent problems associated with the data association, state optimization, sliding window and timestamp alignment in the back-end are discussed in detail. The dataset tests and real substation scene tests are conducted, and the experimental results indicate that the proposed VIO can realize the accurate pose estimation with a favorable initializing efficiency and eminent map representations as expected in concerned environments. The proposed VIO design can therefore be recognized as a preferred tool reference for a class of visual and inertial SLAM application domains preceded by no external location reference support hypothesis.

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