Improving RGB-D SLAM in dynamic environments: A motion removal approach

Sun, Yuxiang; Liu, Ming; Meng, Max Q.-H.

doi:10.1016/j.robot.2016.11.012

Cited by 327 publications

(199 citation statements)

References 19 publications

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“…Motion Segmentation DSLAM [14] Motion Removal DVO-SLAM [16] DynaSLAM (N+G) (RGB-D) SLAM [12] w RGB-D ORB-SLAM2 is initialized and starts the tracking from the very first frame, and hence dynamic objects can introduce errors. ORB-SLAM delays the initialization until there is parallax and consensus using the staticity assumption.…”

Section: Sequence Depth Edgementioning

confidence: 99%

DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes

Bescos

Fácil

Civera

et al. 2018

IEEE Robot. Autom. Lett.

847

569

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The assumption of scene rigidity is typical in SLAM algorithms. Such a strong assumption limits the use of most visual SLAM systems in populated real-world environments, which are the target of several relevant applications like service robotics or autonomous vehicles.In this paper we present DynaSLAM, a visual SLAM system that, building on ORB-SLAM2 [1], adds the capabilities of dynamic object detection and background inpainting. DynaSLAM is robust in dynamic scenarios for monocular, stereo and RGB-D configurations. We are capable of detecting the moving objects either by multi-view geometry, deep learning or both. Having a static map of the scene allows inpainting the frame background that has been occluded by such dynamic objects. We evaluate our system in public monocular, stereo and RGB-D datasets. We study the impact of several accuracy/speed trade-offs to assess the limits of the proposed methodology. Dy-naSLAM outperforms the accuracy of standard visual SLAM baselines in highly dynamic scenarios. And it also estimates a map of the static parts of the scene, which is a must for long-term applications in real-world environments.

show abstract

Section: Sequence Depth Edgementioning

confidence: 99%

DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes

Bescos

Fácil

Civera

et al. 2018

IEEE Robot. Autom. Lett.

847

569

View full text Add to dashboard Cite

show abstract

“…For example, a person who remains still for a long time may be incorrectly considered to be static and added to the map. The third method uses background [18] or foreground [19] detection algorithms. Most of its implementations rely on GPUs and cannot run in real time.…”

Section: Dynamic Pixels Detectionmentioning

confidence: 99%

DRE-SLAM: Dynamic RGB-D Encoder SLAM for a Differential-Drive Robot

Yang¹,

Bi²,

Wang³

et al. 2019

Remote Sensing

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The state-of-the-art visual simultaneous localization and mapping (V-SLAM) systems have high accuracy localization capabilities and impressive mapping effects. However, most of these systems assume that the operating environment is static, thereby limiting their application in the real dynamic world. In this paper, by fusing the information of an RGB-D camera and two encoders that are mounted on a differential-drive robot, we aim to estimate the motion of the robot and construct a static background OctoMap in both dynamic and static environments. A tightly coupled feature-based method is proposed to fuse the two types of information based on the optimization. Dynamic pixels occupied by dynamic objects are detected and culled to cope with dynamic environments. The ability to identify the dynamic pixels on both predefined and undefined dynamic objects is available, which is attributed to the combination of the CPU-based object detection method and a multiview constraint-based approach. We first construct local sub-OctoMaps by using the keyframes and then fuse the sub-OctoMaps into a full OctoMap. This submap-based approach gives the OctoMap the ability to deform, and significantly reduces the map updating time and memory costs. We evaluated the proposed system in various dynamic and static scenes. The results show that our system possesses competitive pose accuracy and high robustness, as well as the ability to construct a clean static OctoMap in dynamic scenes.

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“…To mitigate the effects of dynamic objects on the accuracy of VINS, the major research streams include 1) dynamic objects detection based on motion tracking; 2) moving objects detection and removal based on deep learning; 3) mitigate the effects of dynamic objects using robust methods. The motion tracking method [12,13] is proposed to mitigate the effects of the dynamic objects by detecting and remodel the features belongs to the dynamic objects. Generally, the principle is to identify the features or pixels that are associated with moving objects.…”

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.

show abstract

Improving RGB-D SLAM in dynamic environments: A motion removal approach

Cited by 327 publications

References 19 publications

DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes

DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes

DRE-SLAM: Dynamic RGB-D Encoder SLAM for a Differential-Drive Robot

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

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