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

Abstract

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 either the motion of the robot 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.

arXiv comment30 pages, Published in Neurocomputing

References in corpus (9)

• ORB-SLAM: a Versatile and Accurate Monocular SLAM System
• YOLOv3: An Incremental Improvement
• DeepVO: Towards End-to-End Visual Odometry with Deep Recurrent Convolutional Neural Networks
• The TUM VI Benchmark for Evaluating Visual-Inertial Odometry
• A Photometrically Calibrated Benchmark For Monocular Visual Odometry
• LIFT: Learned Invariant Feature Transform
• UnDeepVO: Monocular Visual Odometry through Unsupervised Deep Learning
• DF-SLAM: A Deep-Learning Enhanced Visual SLAM System based on Deep Local Features
• Self-Improving Visual Odometry

Cited by in corpus (7)

• Visual SLAM: What are the Current Trends and What to Expect?
• AirSLAM: An Efficient and Illumination-Robust Point-Line Visual SLAM System
• From SLAM to Situational Awareness: Challenges and Survey
• AirVO: An Illumination-Robust Point-Line Visual Odometry
• Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach
• Monocular visual simultaneous localization and mapping: (r)evolution from geometry to deep learning-based pipelines
• Marker-based Visual SLAM leveraging Hierarchical Representations