scholarly journals Visual SLAM Based on Dynamic Object Removal

2020 ◽  
Author(s):  
Guoliang Liu
Keyword(s):  
Moving Objects ◽  
State Of The Art ◽  
Service Robot ◽  
Visual Slam ◽  
Dynamic Object ◽  
Dynamic Features ◽  
Dynamic Scenes ◽  
The Core ◽  
Localization And Mapping ◽  
Strong Hypothesis

Visual simultaneous localization and mapping (SLAM) is the core of intelligent robot navigation system. Many traditional SLAM algorithms assume that the scene is static. When a dynamic object appears in the environment, the accuracy of visual SLAM can degrade due to the interference of dynamic features of moving objects. This strong hypothesis limits the SLAM applications for service robot or driverless car intherealdynamicenvironment.Inthispaper,adynamicobject removal algorithm that combines object recognition and optical flow techniques is proposed in the visual SLAM framework for dynamic scenes. The experimental results show that our new method can detect moving object effectively and improve the SLAM performance compared to the state of the art methods.<br>

scholarly journals Visual SLAM Based on Dynamic Object Removal

2020 ◽  
Author(s):  
Guoliang Liu
Keyword(s):  
Moving Objects ◽  
State Of The Art ◽  
Service Robot ◽  
Visual Slam ◽  
Dynamic Object ◽  
Dynamic Features ◽  
Dynamic Scenes ◽  
The Core ◽  
Localization And Mapping ◽  
Strong Hypothesis

Visual simultaneous localization and mapping (SLAM) is the core of intelligent robot navigation system. Many traditional SLAM algorithms assume that the scene is static. When a dynamic object appears in the environment, the accuracy of visual SLAM can degrade due to the interference of dynamic features of moving objects. This strong hypothesis limits the SLAM applications for service robot or driverless car intherealdynamicenvironment.Inthispaper,adynamicobject removal algorithm that combines object recognition and optical flow techniques is proposed in the visual SLAM framework for dynamic scenes. The experimental results show that our new method can detect moving object effectively and improve the SLAM performance compared to the state of the art methods.<br>

scholarly journals Moving Object Tracking for SLAM-based Augmented Reality

2021 ◽  
Author(s):  
Douglas Coelho Braga de Oliveira ◽  
Rodrigo Luis de Souza da Silva
Keyword(s):  
Augmented Reality ◽  
Moving Objects ◽  
Recent Literature ◽  
State Of The Art ◽  
Recognition System ◽  
Virtual Object ◽  
Total Occlusion ◽  
Optimization Framework ◽  
Localization And Mapping ◽  
Potential Applications

Augmented Reality (AR) systems based on the Simultaneous Localization and Mapping (SLAM) problem have received much attention in the last few years. SLAM allows AR applications on unprepared environments, i.e., without markers. However, by eliminating the marker object, we lose the referential for virtual object projection and the main source of interaction between real and virtual elements. In the recent literature, we found works that integrate an object recognition system to the SLAM in a way the objects are incorporated into the map. In this work, we propose a novel optimization framework for an object-aware SLAM system capable of simultaneously estimating the camera and moving objects positioning in the map. In this way, we can combine the advantages of both marker- and SLAM-based methods. We implement our proposed framework over state-of-the-art SLAM software and demonstrate potential applications for AR like the total occlusion of the marker object.

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.

Robust Semantic Mapping in Challenging Environments

Robotica ◽  
2019 ◽  
Vol 38 (2) ◽  
pp. 256-270 ◽  
Author(s):  
Jiyu Cheng ◽  
Yuxiang Sun ◽  
Max Q.-H. Meng
Keyword(s):  
Semantic Information ◽  
Moving Objects ◽  
Dynamic Environments ◽  
Semantic Mapping ◽  
Visual Slam ◽  
The Public ◽  
Semantic Map ◽  
Recent Success ◽  
3D Space ◽  
Localization And Mapping

SummaryVisual simultaneous localization and mapping (visual SLAM) has been well developed in recent decades. To facilitate tasks such as path planning and exploration, traditional visual SLAM systems usually provide mobile robots with the geometric map, which overlooks the semantic information. To address this problem, inspired by the recent success of the deep neural network, we combine it with the visual SLAM system to conduct semantic mapping. Both the geometric and semantic information will be projected into the 3D space for generating a 3D semantic map. We also use an optical-flow-based method to deal with the moving objects such that our method is capable of working robustly in dynamic environments. We have performed our experiments in the public TUM dataset and our recorded office dataset. Experimental results demonstrate the feasibility and impressive performance of the proposed method.

scholarly journals LIFT-SLAM: a deep-learning feature-based monocular visual SLAM method

2020 ◽  
Author(s):  
Hudson Bruno ◽  
Esther Colombini
Keyword(s):  
Deep Learning ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Parameter Tuning ◽  
Robot Motion ◽  
Visual Slam ◽  
Feature Descriptors ◽  
Localization And Mapping ◽  
Feature Based

The Simultaneous Localization and Mapping (SLAM) problem addresses the possibility of a robot to localize itself in an unknown environment and simultaneously build a consistent map of this environment. Recently, cameras have been successfully used to get the environment’s features to perform SLAM, which is referred to as visual SLAM (VSLAM). However, classical VSLAM algorithms can be easily induced to fail when the robot motion or the environment is too challenging. Although new approaches based on Deep Neural Networks (DNNs) have achieved promising results in VSLAM, they still are unable to outperform traditional methods. To leverage the robustness of deep learning to enhance traditional VSLAM systems, we propose to combine the potential of deep learning-based feature descriptors with the traditional geometry-based VSLAM, building a new VSLAM system called LIFT-SLAM. Experiments conducted on KITTI and Euroc datasets show that deep learning can be used to improve the performance of traditional VSLAM systems, as the proposed approach was able to achieve results comparable to the state-of-the-art while being robust to sensorial noise. We enhance the proposed VSLAM pipeline by avoiding parameter tuning for specific datasets with an adaptive approach while evaluating how transfer learning can affect the quality of the features extracted.

scholarly journals LiDAR Odometry and Mapping Based on Semantic Information for Outdoor Environment

2021 ◽  
Vol 13 (15) ◽  
pp. 2864
Author(s):  
Shitong Du ◽  
Yifan Li ◽  
Xuyou Li ◽  
Menghao Wu
Keyword(s):  
Feature Extraction ◽  
Semantic Information ◽  
State Of The Art ◽  
Outdoor Environment ◽  
Geometric Features ◽  
Dynamic Object ◽  
Object Removal ◽  
Localization And Mapping ◽  
High Level ◽  
Crucial Part

Simultaneous Localization and Mapping (SLAM) in an unknown environment is a crucial part for intelligent mobile robots to achieve high-level navigation and interaction tasks. As one of the typical LiDAR-based SLAM algorithms, the Lidar Odometry and Mapping in Real-time (LOAM) algorithm has shown impressive results. However, LOAM only uses low-level geometric features without considering semantic information. Moreover, the lack of a dynamic object removal strategy limits the algorithm to obtain higher accuracy. To this end, this paper extends the LOAM pipeline by integrating semantic information into the original framework. Specifically, we first propose a two-step dynamic objects filtering strategy. Point-wise semantic labels are then used to improve feature extraction and searching for corresponding points. We evaluate the performance of the proposed method in many challenging scenarios, including highway, country and urban from the KITTI dataset. The results demonstrate that the proposed SLAM system outperforms the state-of-the-art SLAM methods in terms of accuracy and robustness.

scholarly journals Object-Level Semantic Map Construction for Dynamic Scenes

2021 ◽  
Vol 11 (2) ◽  
pp. 645
Author(s):  
Xujie Kang ◽  
Jing Li ◽  
Xiangtao Fan ◽  
Hongdeng Jian ◽  
Chen Xu
Keyword(s):  
Moving Objects ◽  
Dynamic Environments ◽  
Dynamic Object ◽  
Dynamic Scenes ◽  
Current Frame ◽  
Semantic Map ◽  
Pose Tracking ◽  
Camera Pose ◽  
Object Level ◽  
Instance Segmentation

Visual simultaneous localization and mapping (SLAM) is challenging in dynamic environments as moving objects can impair camera pose tracking and mapping. This paper introduces a method for robust dense bject-level SLAM in dynamic environments that takes a live stream of RGB-D frame data as input, detects moving objects, and segments the scene into different objects while simultaneously tracking and reconstructing their 3D structures. This approach provides a new method of dynamic object detection, which integrates prior knowledge of the object model database constructed, object-oriented 3D tracking against the camera pose, and the association between the instance segmentation results on the current frame data and an object database to find dynamic objects in the current frame. By leveraging the 3D static model for frame-to-model alignment, as well as dynamic object culling, the camera motion estimation reduced the overall drift. According to the camera pose accuracy and instance segmentation results, an object-level semantic map representation was constructed for the world map. The experimental results obtained using the TUM RGB-D dataset, which compares the proposed method to the related state-of-the-art approaches, demonstrating that our method achieves similar performance in static scenes and improved accuracy and robustness in dynamic scenes.

An Evaluation of Embedded GPU Systems for Visual SLAM Algorithms

2020 ◽  
Vol 2020 (6) ◽  
pp. 325-1-325-6 ◽  
Author(s):  
Tao Peng ◽  
Dingnan Zhang ◽  
Don Lahiru Nirmal Hettiarachchi ◽  
John Loomis
Keyword(s):  
Mobile Robot ◽  
High Performance ◽  
State Of The Art ◽  
Performance Evaluations ◽  
Low Power Consumption ◽  
Visual Slam ◽  
Embedded Computing ◽  
Localization And Mapping ◽  
Battery Capacity ◽  
Quantitative Performance

Simultaneous Localization and Mapping (SLAM) solves the computational problem of estimating the location of a robot and the map of the environment. SLAM is widely used in the area of navigation, odometry, and mobile robot mapping. However, the performance and efficiency of the small industrial mobile robots and unmanned aerial vehicles (UAVs) are highly constrained to the battery capacity. Therefore, a mobile robot, especially a UAV, requires low power consumption while maintaining high performance. This paper demonstrates holistic and quantitative performance evaluations of embedded computing devices that run on the Nvidia Jetson platform. Evaluations are based on the execution of two state-of-the-art Visual SLAM algorithms, ORB-SLAM2 and OpenVSLAM, on Nvidia Jetson Nano, Nvidia Jetson TX2, and Nvidia Jetson Xavier.

An Efficient Spatiotemporal Approach for Moving Object Detection in Dynamic Scenes

2017 ◽  
Vol 12 (3) ◽  
pp. 62-73 ◽  
Author(s):  
Min Liu ◽  
Yang Liu ◽  
Cong Liu ◽  
Juan Wang ◽  
Minghu Wu
Keyword(s):  
Moving Objects ◽  
State Of The Art ◽  
Adaptive Method ◽  
Parameters Estimation ◽  
Dynamic Scenes ◽  
Step Method ◽  
Spatiotemporal Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
New Framework

The dynamic texture (DT) which treats the transient video process a sample from the spatiotemporal model, has shown the surprising performance for moving objects detection in the scenes with the background motions (e.g., swaying branches, falling snow, waving water). However, DT parameters estimation is based on batch-PCA, which is a computationally inefficient method for high-dimensional vectors. Besides, in the realm of DT, the dimension of state space is given or set experimentally. In this work, the authors present a new framework to address these issues. First, they introduce a two-step method, which combines batch-PCA and the increment PCA (IPCA) to estimate the DT parameters in a micro video element (MVE) group. The parameters of the first DT are learned with the batch-PCA as the basis parameters. Parameters of the remaining DTs are estimated by IPCA with the basis parameters and the arriving observation vectors. Second, inspired by the concept of “Observability” from the control theory, the authors extend an adaptive method for salient motion detection according to the increment of singular entropy (ISE). The proposed scheme is tested in various scenes. Its computational efficiency outperforms the state-of-the-art methods and the Equal Error Rate (EER) is lower than other methods.

scholarly journals Improved simultaneous localization and mapping algorithm combined with semantic segmentation model

2021 ◽  
Vol 17 (4) ◽  
pp. 155014772110141
Author(s):  
Xuerong Cui ◽  
Shengjie Xue ◽  
Juan Li ◽  
Shibao Li ◽  
Jianhang Liu ◽  
...  
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Semantic Segmentation ◽  
Indoor Navigation ◽  
Dynamic Object ◽  
Dynamic Features ◽  
Processing Stage ◽  
Convolutional Network ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Dynamic Objects

In the past decades, emerging technologies such as unmanned driving and indoor navigation have developed rapidly, and simultaneous localization and mapping has played unparalleled roles as core technologies. However, dynamic objects in complex environments will affect the positioning accuracy. In order to reduce the influence of dynamic objects, this article proposes an improved simultaneous localization and mapping algorithm combined with semantic segmentation model. First, in the pre-processing stage, in order to reduce the influence of dynamic features, fully convolutional network model is used to find the dynamic object, and then the output image is masked and fused to obtain the final image without dynamic object features. Second, in the feature-processing stage, three parts are improved to reduce the computing complexity, which are extracting, matching, and eliminating mismatching feature points. Experiments show that the absolute trajectory accuracy in high dynamic scene is improved by 48.58% on average. Meanwhile, the average processing time is also reduced by 21.84%.

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