GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

Tang, J. K. K.; Ericson, Ludvig; Folkesson, John; Jensfelt, Patric

doi:10.1109/lra.2019.2927954

Cited by 118 publications

(84 citation statements)

References 45 publications

(81 reference statements)

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“…Based on depth estimated by DEM, our SLAM achieves greater improvement in accuracy on the TUM dataset than others such as SLAM [10]- [12], [17], [18]. Additionally, our SLAM is low-cost as compared with RGBD camera-based SLAM [19], [20]. The results demonstrate that the DEM and DEM-based SLAM are effective.…”

Section: Introductionmentioning

confidence: 84%

“…Existing visual SLAM often relies on data from stereo cameras, monocular cameras [10], [11], [17], [18], [39], or RGBD cameras [12], [19], [20]. In research [10]- [12], [20], [39], the SLAM systems all employ deep learning techniques to extract features. Specifically, in approaches [12], [20], keypoints and descriptors are learned by CNN and RNN (recurrent neural network) for SLAM.…”

Section: Related Workmentioning

confidence: 99%

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Learning Depth for Scene Reconstruction Using an Encoder-Decoder Model

Liu

et al. 2020

IEEE Access

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Depth estimation has received considerable attention and is often applied to visual simultaneous localization and mapping (SLAM) for scene reconstruction. At least to our knowledge, sufficiently reliable depth always fails to be provided for monocular depth estimation-based SLAM because new image features are rarely re-exploited effectively, local features are easily lost, and relative depth relationships among depth pixels are readily ignored in previous depth estimation methods. Based on inaccurate monocular depth estimation, SLAM still faces scale ambiguity problems. To accurately achieve scene reconstruction based on monocular depth estimation, this paper makes three contributions. (1) We design a depth estimation model (DEM), consisting of a precise encoder to re-exploit new features and a decoder to learn local features effectively. (2) We propose a loss function using the depth relationship of pixels to guide the training of DEM. (3) We design a modular SLAM system containing DEM, feature detection, descriptor computation, feature matching, pose prediction, keyframe extraction, loop closure detection, and pose-graph optimization for pixel-level scene reconstruction. Extensive experiments demonstrate that the DEM and DEM-based SLAM are effective. (1) Our DEM predicts more reliable depth than the state of the arts when inputs are RGB images, sparse depth, or the fusion of both on public datasets. (2) The DEM-based SLAM system achieves comparable accuracy as compared with well-known modular SLAM systems. INDEX TERMS Convolutional neural networks, depth estimation, decoder, encoder, simultaneous localization and mapping.

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Section: Introductionmentioning

confidence: 84%

Section: Related Workmentioning

confidence: 99%

Learning Depth for Scene Reconstruction Using an Encoder-Decoder Model

Liu

et al. 2020

IEEE Access

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“…pure rotation) haven't been carefully handled; 2) More indepth and detailed research on the convergent properties of the inverse depth of the map point is needed; 3) Our approach adopts the classic ORB features of image in the whole SLAM framework. With the development of deep learning for feature extraction, such as the MagicPoint [27] and GCN features [28], in future we will try to replace the ORB features with the deep-learning features to further improve the robust of our system.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

SDF-SLAM: Semantic Depth Filter SLAM for Dynamic Environments

Cui

2020

IEEE Access

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Simultaneous Localization and Mapping (SLAM) has been widely applied in computer vision and robotics. For the dynamic environments which are very common in the real word, traditional visual SLAM system faces significant drop in localization and mapping accuracy due to the static world assumption. Recently, the semantic visual SLAM systems towards dynamic scenes have gradually attracted more and more attentions, which use the semantic information of images to help remove dynamic feature points. Existing semantic visual SLAM systems commonly detect the dynamic feature points by the semantic prior, geometry constraint or the combine of them, then map points corresponding to dynamic feature points are removed. In the visual SLAM framework, pose calculation is essentially around the 3D map points, so the essence of improving the accuracy of visual SLAM system is to build a more accurate and reliable map. These existing semantic visual SLAM systems are actually adopting an indirect way to acquire reliable map points, and several drawbacks exist. In this paper, we present SDF-SLAM: Semantic Depth Filter SLAM, a visual semantic SLAM system towards dynamic environments, which utilizes the technology of depth filter to directly judge whether a 3D map point is dynamic or not. First, the semantic information is integrated into the original pure geometry SLAM system by the semantic optical flow method to perform reliable map initialization. Second, design the semantic depth filter that satisfies the Gaussian Uniform mixture distribution to describe the inverse depth of each map point. Third, updating the inverse depth of 3D map point in a Bayesian estimation framework, and dividing the 3D map point into active one or inactive one. Last, only the active map points are utilized to achieve robust camera pose tracking. Experiments on TUM dataset demonstrate that our approach outperforms original ORB-SLAM2 and other state-of-the-art semantic SLAM systems.

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“…Global consistency is then achieved by means of loop closure detection and global pose graph optimization. Another variation of ORB-SLAM2 can be found in [121], where ORB features were replaced by learnt point features, referred to as GCNv2. It was demonstrated that the proposed approach has comparable performance to ORB-SLAM2 in most scenarios, but performs slightly better in case of fast rotations.…”

Section: Resilience To Feature Detection/association Failurementioning

confidence: 99%

Feature-based visual simultaneous localization and mapping: a survey

Azzam

Taha²,

Huang

et al. 2020

SN Appl. Sci.

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Visual simultaneous localization and mapping (SLAM) has attracted high attention over the past few years. In this paper, a comprehensive survey of the state-of-the-art feature-based visual SLAM approaches is presented. The reviewed approaches are classified based on the visual features observed in the environment. Visual features can be seen at different levels; low-level features like points and edges, middle-level features like planes and blobs, and high-level features like semantically labeled objects. One of the most critical research gaps regarding visual SLAM approaches concluded from this study is the lack of generality. Some approaches exhibit a very high level of maturity, in terms of accuracy and efficiency. Yet, they are tailored to very specific environments, like feature-rich and static environments. When operating in different environments, such approaches experience severe degradation in performance. In addition, due to software and hardware limitations, guaranteeing a robust visual SLAM approach is extremely challenging. Although semantics have been heavily exploited in visual SLAM, understanding of the scene by incorporating relationships between features is not yet fully explored. A detailed discussion of such research challenges is provided throughout the paper.

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GCNv2: Efficient Correspondence Prediction for Real-Time SLAM

Cited by 118 publications

References 45 publications

Learning Depth for Scene Reconstruction Using an Encoder-Decoder Model

Learning Depth for Scene Reconstruction Using an Encoder-Decoder Model

SDF-SLAM: Semantic Depth Filter SLAM for Dynamic Environments

Feature-based visual simultaneous localization and mapping: a survey

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