Search Results for author:
Found 17 papers, 4 papers with code
High Frequency, High Accuracy Pointing onboard Nanosats using Neuromorphic Event Sensing and Piezoelectric Actuation
In this work, we develop a novel payload that utilises a neuromorphic event sensor (for high frequency and highly accurate relative attitude estimation) paired in a closed loop with a piezoelectric stage (for active attitude corrections) to provide highly stable sensor-specific pointing.
Globally Optimal Event-Based Divergence Estimation for Ventral Landing
This is achieved by estimating divergence (inverse TTC), which is the rate of radial optic flow, from the event stream generated during landing.
Towards Bridging the Space Domain Gap for Satellite Pose Estimation using Event Sensing
Deep models trained using synthetic data require domain adaptation to bridge the gap between the simulation and target environments.
Asynchronous Optimisation for Event-based Visual Odometry
Event cameras open up new possibilities for robotic perception due to their low latency and high dynamic range.
Learning to Predict Repeatability of Interest Points
Many robotics applications require interest points that are highly repeatable under varying viewpoints and lighting conditions.
HM4: Hidden Markov Model with Memory Management for Visual Place Recognition
However, this creates an unboundedly-growing database that poses time and memory scalability challenges for place recognition methods.
Monocular Rotational Odometry with Incremental Rotation Averaging and Loop Closure
To simplify the task of absolute orientation estimation, we formulate the monocular rotational odometry problem and devise a fast algorithm to accurately estimate camera orientations with 2D-2D feature matches alone.
Improved Visual Localization via Graph Smoothing
One solution to this problem is to learn a deep neural network to infer the pose of a query image after learning on a dataset of images with known poses.
Scalable Place Recognition Under Appearance Change for Autonomous Driving
Our experiments show that, compared to state-of-the-art techniques, our method has much greater potential for large-scale place recognition for autonomous driving.
Visual Localization Under Appearance Change: Filtering Approaches
Our approaches rely on local features with an encoding technique to represent an image as a single vector.
Real-Time Monocular Object-Model Aware Sparse SLAM
While sparse point-based SLAM methods provide accurate camera localization, the generated maps lack semantic information.
Structure Aware SLAM using Quadrics and Planes
Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics.
Learning Deeply Supervised Good Features to Match for Dense Monocular Reconstruction
Visual SLAM (Simultaneous Localization and Mapping) methods typically rely on handcrafted visual features or raw RGB values for establishing correspondences between images.
Addressing Challenging Place Recognition Tasks using Generative Adversarial Networks
In the process, meaningful feature spaces are learned for each domain, the distances in which can be used for the task of place recognition.
Robotics
Meaningful Maps With Object-Oriented Semantic Mapping
For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them.
Robotics
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it.
Robotics
Hierarchical Higher-Order Regression Forest Fields: An Application to 3D Indoor Scene Labelling
Specifically, we relax the labelling problem to a regression, and generalize the higher-order associative P n Potts model to a new family of arbitrary higher-order models based on regression forests.
