Sepideh Shamsizadeh

Sepideh Shamsizadeh

AI and Robotics Engineer

Using Docker with ROS Noetic on Ubuntu 20.04: A Comprehensive Guide for Beginners

less than 1 minute read

Published:

Welcome to the exciting world of Docker, where containerization opens up new possibilities in software development and deployment. In this journey, we will unravel the mysteries of Docker, guiding you step-by-step through the fundamentals. But that’s not all — brace yourself for a hands-on experience as we delve into installing Docker alongside ROS Noetic on Ubuntu 20.04.

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By the end of this guide, you’ll not only grasp the core concepts of Docker but also seamlessly integrate it with the Robot Operating System (ROS) Noetic, bringing together the power of containerization and robotics development. Let’s embark on this adventure, unlocking the potential of Docker in your software development endeavors. Continue…

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Blog Post 5

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title: “ROS2 humble” excerpt: “ Docker file and docker build. “ —

Summary of Fully Convolutional Networks for Semantic Segmentation

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The paper discusses Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, and Trevor Darrell from UC Berkeley. The authors introduce fully convolutional networks (FCNs) as a powerful approach for semantic segmentation, surpassing existing methods in pixel-wise prediction. The key innovation is to replace fully-connected layers with convolutional layers, allowing for end-to-end training and efficient inference on arbitrary-sized inputs. The FCN architecture incorporates both deep, coarse semantic information and shallow, fine appearance information for accurate segmentations. The paper reviews related work, emphasizes the efficiency of fully convolutional training over patchwise methods, and demonstrates state-of-the-art results on various datasets. The authors compare their approach to adaptations of deep classification nets for semantic segmentation, highlighting the end-to-end learning capability of FCNs.

Image Segmentation

5 minute read

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Image segmentation is a sophisticated technique in computer vision that involves partitioning an image into distinct, meaningful regions. Its primary objective is to analyze and understand the visual content within an image by assigning specific labels to different parts of the scene. This process aids in extracting valuable information for various applications, such as object recognition, scene understanding, and autonomous navigation.

Summery of PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

7 minute read

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PointNet is a clever deep learning approach for understanding 3D geometric data, like point clouds. Unlike other methods that transform data into regular grids or images, PointNet directly processes point clouds, respecting their irregular nature. It’s a simple yet powerful neural network that handles tasks such as object classification and part segmentation efficiently. The key is using a symmetric function, max pooling, which helps the network select important points in the cloud. It’s not just fast; it’s also robust to changes in input points. The project includes a theoretical analysis, showing PointNet’s ability to approximate various functions, and an experimental evaluation, demonstrating its speed and competitive performance compared to other approaches. The contribution lies in designing a deep net architecture for unordered 3D point sets, showcasing its effectiveness in multiple tasks and providing insights into its stability and efficiency.