Search Results for author:
Found 10 papers, 4 papers with code
Large-scale Autonomous Flight with Real-time Semantic SLAM under Dense Forest Canopy
Semantic maps represent the environment using a set of semantically meaningful objects.
Place Recognition in Forests with Urquhart Tessellations
In this letter, we present a novel descriptor based on Urquhart tessellations derived from the position of trees in a forest.
SLOAM: Semantic Lidar Odometry and Mapping for Forest Inventory
This paper describes an end-to-end pipeline for tree diameter estimation based on semantic segmentation and lidar odometry and mapping.
Large Scale Model Predictive Control with Neural Networks and Primal Active Sets
The approach combines an offline-trained fully-connected neural network with an online primal active set solver.
DFuseNet: Deep Fusion of RGB and Sparse Depth Information for Image Guided Dense Depth Completion
In this paper we propose a convolutional neural network that is designed to upsample a series of sparse range measurements based on the contextual cues gleaned from a high resolution intensity image.
Decentralization of Multiagent Policies by Learning What to Communicate
Effective communication is required for teams of robots to solve sophisticated collaborative tasks.
Robotics
Monocular Camera Based Fruit Counting and Mapping with Semantic Data Association
We present a cheap, lightweight, and fast fruit counting pipeline that uses a single monocular camera.
Robotics
Robust Fruit Counting: Combining Deep Learning, Tracking, and Structure from Motion
We present a novel fruit counting pipeline that combines deep segmentation, frame to frame tracking, and 3D localization to accurately count visible fruits across a sequence of images.
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
Homography estimation between multiple aerial images can provide relative pose estimation for collaborative autonomous exploration and monitoring.
Ranked #4 on
Homography Estimation
on S-COCO
Neural Network Memory Architectures for Autonomous Robot Navigation
This work is a first thorough study of memory structures for deep-neural-network-based robot navigation, and offers novel tools to train such networks from supervision and quantify their ability to generalize to unseen scenarios.
