LIFT-SLAM: A deep-learning feature-based monocular visual SLAM method

Bruno, Hudson Martins Silva; Colombini, Esther Luna

doi:10.1016/j.neucom.2021.05.027

Cited by 68 publications

(34 citation statements)

References 12 publications

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“…Although we noticed that the deep learning-based VSLAM algorithms could be more robust than a traditional VSLAM algorithm, they are still not capable of estimating the pose in some cases. In [28], we show that fine-tuning the DNNs with VO sequences can improve the robustness and the accuracy of the deep learning-based algorithms. This is possible because, with fine-tuning, the DNNs can learn characteristics presented only in VO sequences.…”

Section: Resultsmentioning

confidence: 93%

A Comparative Evaluation of Learned Feature Descriptors on Hybrid Monocular Visual SLAM Methods

Bruno

Colombini

2020

2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Educ

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Classical Visual Simultaneous Localization and Mapping (VSLAM) algorithms can be easily induced to fail when either the robot's motion or the environment is too challenging. The use of Deep Neural Networks to enhance VSLAM algorithms has recently achieved promising results, which we call hybrid methods. In this paper, we compare the performance of hybrid monocular VSLAM methods with different learned feature descriptors. To this end, we propose a set of experiments to evaluate the robustness of the algorithms under different environments, camera motion, and camera sensor noise. Experiments conducted on KITTI and Euroc MAV datasets confirm that learned feature descriptors can create more robust VSLAM systems.

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Section: Resultsmentioning

confidence: 93%

A Comparative Evaluation of Learned Feature Descriptors on Hybrid Monocular Visual SLAM Methods

Bruno

Colombini

2020

2020 Latin American Robotics Symposium (LARS), 2020 Brazilian Symposium on Robotics (SBR) and 2020 Workshop on Robotics in Educ

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“…CNN-SLAM [158] 2017 Monocular CNN Supervised DeepVo [191] 2017 Monocular R-CNN Supervised Code-SLAM [44] 2018 Monocular U-Net Supervised DVSO [159] 2018 Stereo DispNet Semi-supervised UnDeepVo [173] 2018 Monocular VGG encoder-decoder Unsupervised CNN-SVO [192] 2019 Monocular CNN Hybrid VO GANVO [193] 2019 Monocular GAN Unsupervised Li et al [194] 2019 Monocular CNN Supervised D3VO [48] 2020 Monocular CNN Hybrid DeepSeqSLAM [186] 2020 Monocular CNN+RNN Supervised DeepSLAM [145] 2021 Monocular R-CNN Unsupervised LIFT-SLAM [195] 2021 Monocular DNN Supervised Zhang et al [196] 2021 Stereo U-Net encoder-decoder Unsupervised…”

Section: Methods Year Sensor Neural Network Supervisionmentioning

confidence: 99%

An Overview on Visual SLAM: From Tradition to Semantic

et al. 2022

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Visual SLAM (VSLAM) has been developing rapidly due to its advantages of low-cost sensors, the easy fusion of other sensors, and richer environmental information. Traditional visionbased SLAM research has made many achievements, but it may fail to achieve wished results in challenging environments. Deep learning has promoted the development of computer vision, and the combination of deep learning and SLAM has attracted more and more attention. Semantic information, as high-level environmental information, can enable robots to better understand the surrounding environment. This paper introduces the development of VSLAM technology from two aspects: traditional VSLAM and semantic VSLAM combined with deep learning. For traditional VSLAM, we summarize the advantages and disadvantages of indirect and direct methods in detail and give some classical VSLAM open-source algorithms. In addition, we focus on the development of semantic VSLAM based on deep learning. Starting with typical neural networks CNN and RNN, we summarize the improvement of neural networks for the VSLAM system in detail. Later, we focus on the help of target detection and semantic segmentation for VSLAM semantic information introduction. We believe that the development of the future intelligent era cannot be without the help of semantic technology. Introducing deep learning into the VSLAM system to provide semantic information can help robots better perceive the surrounding environment and provide people with higher-level help.

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“…Therefore, most algorithms need auxiliary data from other sensors, such as an inertial measurement unit (IMU) or odometer. In recent years, along with the rapid development of artificial intelligence (AI), learning methods are also used to solve the SLAM problem [46][47][48].…”

Section: Slam Mapping Overviewmentioning

confidence: 99%

Real-Time 3D Mapping in Isolated Industrial Terrain with Use of Mobile Robotic Vehicle

et al. 2022

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Simultaneous localization and mapping (SLAM) is a dual process responsible for the ability of a robotic vehicle to build a map of its surroundings and estimate its position on that map. This paper presents the novel concept of creating a 3D map based on the adaptive Monte-Carlo location (AMCL) and the extended Kalman filter (EKF). This approach is intended for inspection or rescue operations in a closed or isolated area where there is a risk to humans. The proposed solution uses particle filters together with data from on-board sensors to estimate the local position of the robot. Its global position is determined through the Rao–Blackwellized technique. The developed system was implemented on a wheeled mobile robot equipped with a sensing system consisting of a laser scanner (LIDAR) and an inertial measurement unit (IMU), and was tested in the real conditions of an underground mine. One of the contributions of this work is to propose a low-complexity and low-cost solution to real-time 3D-map creation. The conducted experimental trials confirmed that the performance of the three-dimensional mapping was characterized by high accuracy and usefulness for recognition and inspection tasks in an unknown industrial environment.

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LIFT-SLAM: A deep-learning feature-based monocular visual SLAM method

Cited by 68 publications

References 12 publications

A Comparative Evaluation of Learned Feature Descriptors on Hybrid Monocular Visual SLAM Methods

A Comparative Evaluation of Learned Feature Descriptors on Hybrid Monocular Visual SLAM Methods

An Overview on Visual SLAM: From Tradition to Semantic

Real-Time 3D Mapping in Isolated Industrial Terrain with Use of Mobile Robotic Vehicle

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