Search Results for author:
Found 11 papers, 7 papers with code
MAD-ICP: It Is All About Matching Data -- Robust and Informed LiDAR Odometry
Most of these systems implicitly rely on assumptions about the operating environment, the sensor used, and motion pattern.
VBR: A Vision Benchmark in Rome
This paper presents a vision and perception research dataset collected in Rome, featuring RGB data, 3D point clouds, IMU, and GPS data.
Photometric LiDAR and RGB-D Bundle Adjustment
This paper presents a novel BA photometric strategy that accounts for both RGB-D and LiDAR in the same way.
On Domain-Specific Pre-Training for Effective Semantic Perception in Agricultural Robotics
Furthermore, the pre-trained networks obtain similar performance to the fully supervised with less labeled data.
Plug-and-Play SLAM: A Unified SLAM Architecture for Modularity and Ease of Use
Nowadays, SLAM (Simultaneous Localization and Mapping) is considered by the Robotics community to be a mature field.
Robotics
Least Squares Optimization: from Theory to Practice
In this work, we propose a unified methodology to design and develop efficient Least-Squares Optimization algorithms, focusing on the structures and patterns of each specific domain.
Robotics
Adding Cues to Binary Feature Descriptors for Visual Place Recognition
In this paper we propose an approach to embed continuous and selector cues in binary feature descriptors used for visual place recognition.
HBST: A Hamming Distance embedding Binary Search Tree for Visual Place Recognition
Reliable and efficient Visual Place Recognition is a major building block of modern SLAM systems.
A General Framework for Flexible Multi-Cue Photometric Point Cloud Registration
Our approach exploits the different cues in a natural and consistent way and the registration can be done at framerate for a typical range or imaging sensor.
ProSLAM: Graph SLAM from a Programmer's Perspective
In this paper we present ProSLAM, a lightweight stereo visual SLAM system designed with simplicity in mind.
Robotics
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
We compare the classification results obtained using both real and synthetic images as training data.
