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Found 11 papers, 5 papers with code
DM-VIO: Delayed Marginalization Visual-Inertial Odometry
This is the foundation of the proposed pose graph bundle adjustment, which we use for IMU initialization.
MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera
Unlike other multi-view stereo methods, MonoRec is able to reconstruct both static and moving objects by leveraging the predicted masks.
LM-Reloc: Levenberg-Marquardt Based Direct Visual Relocalization
The learned features significantly improve the robustness of direct image alignment, especially for relocalization across different conditions.
4Seasons: A Cross-Season Dataset for Multi-Weather SLAM in Autonomous Driving
We present a novel dataset covering seasonal and challenging perceptual conditions for autonomous driving.
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
We propose D3VO as a novel framework for monocular visual odometry that exploits deep networks on three levels -- deep depth, pose and uncertainty estimation.
Rolling-Shutter Modelling for Direct Visual-Inertial Odometry
The visual part of the system performs a photometric bundle adjustment on a sparse set of points.
GN-Net: The Gauss-Newton Loss for Multi-Weather Relocalization
Direct SLAM methods have shown exceptional performance on odometry tasks.
Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras
We propose a novel real-time direct monocular visual odometry for omnidirectional cameras.
Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization
We present VI-DSO, a novel approach for visual-inertial odometry, which jointly estimates camera poses and sparse scene geometry by minimizing photometric and IMU measurement errors in a combined energy functional.
Real-Time Trajectory Replanning for MAVs using Uniform B-splines and a 3D Circular Buffer
In this paper, we present a real-time approach to local trajectory replanning for microaerial vehicles (MAVs).
Robotics
From Monocular SLAM to Autonomous Drone Exploration
Micro aerial vehicles (MAVs) are strongly limited in their payload and power capacity.
