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Found 10 papers, 4 papers with code
Project Aria: A New Tool for Egocentric Multi-Modal AI Research
Egocentric, multi-modal data as available on future augmented reality (AR) devices provides unique challenges and opportunities for machine perception.
Long-term Visual Map Sparsification with Heterogeneous GNN
We address the problem of map sparsification for long-term visual localization.
Good Graph to Optimize: Cost-Effective, Budget-Aware Bundle Adjustment in Visual SLAM
The cost-efficiency of visual(-inertial) SLAM (VSLAM) is a critical characteristic of resource-limited applications.
Closed-Loop Benchmarking of Stereo Visual-Inertial SLAM Systems: Understanding the Impact of Drift and Latency on Tracking Accuracy
Visual-inertial SLAM is essential for robot navigation in GPS-denied environments, e. g. indoor, underground.
Robotics
Good Feature Matching: Towards Accurate, Robust VO/VSLAM with Low Latency
Analysis of state-of-the-art VO/VSLAM system exposes a gap in balancing performance (accuracy & robustness) and efficiency (latency).
Autonomous, Monocular, Vision-Based Snake Robot Navigation and Traversal of Cluttered Environments using Rectilinear Gait Motion
As a first pass in this direction, we equip a wireless, monocular color camera to the head of a robotic snake.
Low-latency Visual SLAM with Appearance-Enhanced Local Map Building
This paper describes an enhancement to co-visibility local map building by incorporating a strong appearance prior, which leads to a more compact local map and latency reduction in downstream data association.
Characterizing SLAM Benchmarks and Methods for the Robust Perception Age
The ad-hoc creation of these benchmarks does not necessarily illuminate the particular weak points of a SLAM algorithm when performance is evaluated.
Good Feature Selection for Least Squares Pose Optimization in VO/VSLAM
This paper aims to select features that contribute most to the pose estimation in VO/VSLAM.
Robotics
Good Line Cutting: towards Accurate Pose Tracking of Line-assisted VO/VSLAM
This paper tackles a problem in line-assisted VO/VSLAM: accurately solving the least squares pose optimization with unreliable 3D line input.
