Search Results for author:
Found 14 papers, 3 papers with code
Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic
We present the algorithm as a simple modification to RRT* that could be further extended by more advanced path-planning algorithms.
Robotics
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor Navigation
We provide insights into our network predictions and show that our approach can also improve the performances of common localization techniques.
UAV Localization Using Autoencoded Satellite Images
This trained autoencoder is used to compress a real UAV image, which is then compared to the precollected, nearby, autoencoded GE images using an inner-product kernel.
Batch Informed Trees (BIT*): Sampling-based Optimal Planning via the Heuristically Guided Search of Implicit Random Geometric Graphs
In this paper, we present Batch Informed Trees (BIT*), a planning algorithm based on unifying graph- and sampling-based planning techniques.
Robotics
Mapless Online Detection of Dynamic Objects in 3D Lidar
This paper presents a model-free, setting-independent method for online detection of dynamic objects in 3D lidar data.
Robotics
Exactly Sparse Gaussian Variational Inference with Application to Derivative-Free Batch Nonlinear State Estimation
We present a Gaussian Variational Inference (GVI) technique that can be applied to large-scale nonlinear batch state estimation problems.
Multivariate Gaussian Variational Inference by Natural Gradient Descent
This short note reviews so-called Natural Gradient Descent (NGD) for multivariate Gaussians.
Variational Inference with Parameter Learning Applied to Vehicle Trajectory Estimation
Our contribution is to additionally learn parameters of our system models (which may be difficult to choose in practice) within the ESGVI framework.
Variational Inference as Iterative Projection in a Bayesian Hilbert Space with Application to Robotic State Estimation
The goal is to find an approximate probability density function (PDF) from a chosen family that is in some sense 'closest' to the full Bayesian posterior.
Relatively Lazy: Indoor-Outdoor Navigation Using Vision and GNSS
Adding additional absolute sensors such as Global Navigation Satellite Systems (GNSS) has the potential to expand the domain of Visual Teach and Repeat to environments where the ability to visually localize is not guaranteed.
Unsupervised Learning of Lidar Features for Use in a Probabilistic Trajectory Estimator
Though the framework is general to any form of parameter learning and sensor modality, we demonstrate application to feature and uncertainty learning with a deep network for 3D lidar odometry.
Variational Inference
Robotics
Self-Supervised Feature Learning for Long-Term Metric Visual Localization
Visual localization is the task of estimating camera pose in a known scene, which is an essential problem in robotics and computer vision.
Pointing the Way: Refining Radar-Lidar Localization Using Learned ICP Weights
Combining a proven analytical approach with a learned weight reduces localization errors in radar-lidar ICP results run on real-world autonomous driving data by up to 54. 94% in translation and 68. 39% in rotation, while maintaining interpretability and robustness.
Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Algorithm
This paper presents a novel method to assess the resilience of the Iterative Closest Point (ICP) algorithm via deep-learning-based attacks on lidar point clouds.
