27 citations · 48 across the 6 of their papers we have counts for
6 papers
Visual-Inertial SLAM as Simple as A, B, VINS
Nathaniel Merrill, Guoquan Huang
We present AB-VINS, a different kind of visual-inertial SLAM system. Unlike most popular VINS methods which only use hand-crafted techniques, AB-VINS makes use of three different d…
Incremental Dense Reconstruction from Monocular Video with Guided Sparse Feature Volume Fusion
Xingxing Zuo, Nan Yang, Nathaniel Merrill +2
Incrementally recovering 3D dense structures from monocular videos is of paramount importance since it enables various robotics and AR applications. Feature volumes have recently b…
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation
Nathaniel Merrill, Yuliang Guo, Xingxing Zuo +5
We propose a keypoint-based object-level SLAM framework that can provide globally consistent 6DoF pose estimates for symmetric and asymmetric objects alike. To the best of our know…
CodeVIO: Visual-Inertial Odometry with Learned Optimizable Dense Depth
Xingxing Zuo, Nathaniel Merrill, Wei Li +3
In this work, we present a lightweight, tightly-coupled deep depth network and visual-inertial odometry (VIO) system, which can provide accurate state estimates and dense depth map…
CALC2.0: Combining Appearance, Semantic and Geometric Information for Robust and Efficient Visual Loop Closure
Nathaniel Merrill, Guoquan Huang
Traditional attempts for loop closure detection typically use hand-crafted features, relying on geometric and visual information only, whereas more modern approaches tend to use se…
Lightweight Unsupervised Deep Loop Closure
Nate Merrill, Guoquan Huang
Robust efficient loop closure detection is essential for large-scale real-time SLAM. In this paper, we propose a novel unsupervised deep neural network architecture of a feature em…