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Found 13 papers, 8 papers with code
Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion
To address this issue, we propose a novel stereo-LiDAR depth estimation network with Semi-Dense hint Guidance, named SDG-Depth.
TextSLAM: Visual SLAM with Semantic Planar Text Features
The results show that integrating texture features leads to a more superior SLAM system that can match images across day and night.
FeatureBooster: Boosting Feature Descriptors with a Lightweight Neural Network
We introduce a lightweight network to improve descriptors of keypoints within the same image.
M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots
We introduce M2DGR: a novel large-scale dataset collected by a ground robot with a full sensor-suite including six fish-eye and one sky-pointing RGB cameras, an infrared camera, an event camera, a Visual-Inertial Sensor (VI-sensor), an inertial measurement unit (IMU), a LiDAR, a consumer-grade Global Navigation Satellite System (GNSS) receiver and a GNSS-IMU navigation system with real-time kinematic (RTK) signals.
Multi-instance Point Cloud Registration by Efficient Correspondence Clustering
We address the problem of estimating the poses of multiple instances of the source point cloud within a target point cloud.
StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation
Inspired by the early works on indoor modeling, we leverage the structural regularities exhibited in indoor scenes, to train a better depth network.
Attention-SLAM: A Visual Monocular SLAM Learning from Human Gaze
This paper proposes a novel simultaneous localization and mapping (SLAM) approach, namely Attention-SLAM, which simulates human navigation mode by combining a visual saliency model (SalNavNet) with traditional monocular visual SLAM.
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors
The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91. 15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method.
TextSLAM: Visual SLAM with Planar Text Features
We propose to integrate text objects in man-made scenes tightly into the visual SLAM pipeline.
StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments
Instead of using Manhattan world assumption, we use Atlanta world model to describe such regularity.
Robotics
Efficient Outlier Removal in Large Scale Global Structure-from-Motion
This work addresses the outlier removal problem in large-scale global structure-from-motion.
Active Image-based Modeling with a Toy Drone
Image-based modeling techniques can now generate photo-realistic 3D models from images.
Simultaneous Video Defogging and Stereo Reconstruction
We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence.
