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

Bahraini, Masoud; Rad, A.B.; Bozorg, Mohammad

doi:10.3390/s19173699

Cited by 41 publications

(29 citation statements)

References 36 publications

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“…This may result in tracking loss or initialization failure due to a small number of remaining feature points. Masoud et al [38] propose an algorithm that integrates the feature-based SLAM with multi-target tracking (MTT) for dynamic environments. They use Faster R-CNN [39] to detect objects and classify them as moving and stationary objects.…”

Section: Slam Combined With Deep Learningmentioning

confidence: 99%

DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes

Cheng

Wang

Zhou

et al. 2020

IJGI

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Most Simultaneous Localization and Mapping (SLAM) methods assume that environments are static. Such a strong assumption limits the application of most visual SLAM systems. The dynamic objects will cause many wrong data associations during the SLAM process. To address this problem, a novel visual SLAM method that follows the pipeline of feature-based methods called DM-SLAM is proposed in this paper. DM-SLAM combines an instance segmentation network with optical flow information to improve the location accuracy in dynamic environments, which supports monocular, stereo, and RGB-D sensors. It consists of four modules: semantic segmentation, ego-motion estimation, dynamic point detection and a feature-based SLAM framework. The semantic segmentation module obtains pixel-wise segmentation results of potentially dynamic objects, and the ego-motion estimation module calculates the initial pose. In the third module, two different strategies are presented to detect dynamic feature points for RGB-D/stereo and monocular cases. In the first case, the feature points with depth information are reprojected to the current frame. The reprojection offset vectors are used to distinguish the dynamic points. In the other case, we utilize the epipolar constraint to accomplish this task. Furthermore, the static feature points left are fed into the fourth module. The experimental results on the public TUM and KITTI datasets demonstrate that DM-SLAM outperforms the standard visual SLAM baselines in terms of accuracy in highly dynamic environments.

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Section: Slam Combined With Deep Learningmentioning

confidence: 99%

DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes

Cheng

Wang

Zhou

et al. 2020

IJGI

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“…During the image registration phase, the images are aligned with each other based on matched features. Different techniques for feature matching have been used in previous work, and among the most well-known ones are Fast Library for Approximate Nearest Neighbors (FLANN), Brute-Force Matcher, and Random Sample Consensus RANSAC [27]. For example, Shi et al [28] described an image stitching algorithm based on parallax improved feature blocks (PIFB).…”

Section: Related Workmentioning

confidence: 99%

Automatic 360° Mono-Stereo Panorama Generation Using a Cost-Effective Multi-Camera System

Ullah

Zia

Kim³

et al. 2020

Sensors

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In recent years, 360° videos have gained the attention of researchers due to their versatility and applications in real-world problems. Also, easy access to different visual sensor kits and easily deployable image acquisition devices have played a vital role in the growth of interest in this area by the research community. Recently, several 360° panorama generation systems have demonstrated reasonable quality generated panoramas. However, these systems are equipped with expensive image sensor networks where multiple cameras are mounted in a circular rig with specific overlapping gaps. In this paper, we propose an economical 360° panorama generation system that generates both mono and stereo panoramas. For mono panorama generation, we present a drone-mounted image acquisition sensor kit that consists of six cameras placed in a circular fashion with optimal overlapping gap. The hardware of our proposed image acquisition system is configured in such way that no user input is required to stitch multiple images. For stereo panorama generation, we propose a lightweight, cost-effective visual sensor kit that uses only three cameras to cover 360° of the surroundings. We also developed stitching software that generates both mono and stereo panoramas using a single image stitching pipeline where the panorama generated by our proposed system is automatically straightened without visible seams. Furthermore, we compared our proposed system with existing mono and stereo contents generation systems in both qualitative and quantitative perspectives, and the comparative measurements obtained verified the effectiveness of our system compared to existing mono and stereo generation systems.

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“…The straight forward method to mitigate the effects of dynamic objects is to detect and remove the features belong to the dynamic objects from visual simultaneous localization and mapping (SLAM) [20,21]. Due to the many dynamic objects in complex environments, a detect-SLAM system [22] is proposed to integrate SLAM with a deep neural network to detect the moving objects and remove the unreliable features from moving objects.…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, an SSD detector [24] is presented to detect moving objects with prior knowledge, and selection tracking algorithm is proposed to eliminate dynamic objects. In addition, an ML-RANSAC algorithm [21] proposes to distinguish moving from stationary objects and classify the outliers belonging to moving objects. Although significant researches have been made in object detection [25][26][27], there are still many challenges in dynamic object detection.…”

Section: Introductionmentioning

confidence: 99%

Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

Bai

Wen

Hsu

2020

Preprint

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Visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature tracking process which is fatal to the feature-based VINS. The well-known method mitigates the effects of dynamic objects is to detect the vehicles using deep neural networks and remove the features belongs to the surrounding vehicle. However, excessive exclusion of features can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this paper proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of VINS. Firstly, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurements by integrating two parts: 1) the geometry of feature distribution (GFD), 2) the quality of feature tracking. Secondly, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the impacts of outlier measurements, such as the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance of excessive parameterization of M-estimator. Experiments are conducted in a typical urban area of Hong Kong with numerous dynamic objects, and the results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of VINS is obtained when compared with the conventional method.

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

Cited by 41 publications

References 36 publications

DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes

DM-SLAM: A Feature-Based SLAM System for Rigid Dynamic Scenes

Automatic 360° Mono-Stereo Panorama Generation Using a Cost-Effective Multi-Camera System

Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

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