Introduction: Autonomous Mobile Robots (AMRs) are redefining industries, from logistics

What are the commonalities among data entry mistakes, laborious workloads,

Exploration is a crucial aspect of autonomous navigation in robotics. Simultaneous Localization and Mapping (SLAM) is a fundamental technique that enables robots to navigate and map unknown environments. Visual Simultaneous Localization and Mapping, or Visual SLAM (VSLAM), is a specific type of SLAM that enables robots to map their surroundings and estimate their own position, i.e., odometry, in real-time using visual input from cameras.

Simulated data is increasingly popular for training and deploying deep learning models. Advances in simulation technology enable generating realistic synthetic data for applications such as robotics, autonomous vehicles, and computer vision. Although simulated data is cost-effective and efficient, deploying models in the real world requires careful consideration of transferability and generalization.

Autonomous mobile robot (AMR) navigation relies heavily on visual SLAM, which stands for visual simultaneous localization and mapping. This technique uses a camera to estimate the robot’s position while simultaneously creating a map of the environment. Visual SLAM is crucial for Mars exploration devices, unmanned robots, endoscopy, and vacuum cleaning.

ROS is an open-source framework that simplifies the development of complex robotic applications. It utilizes a distributed architecture that facilitates communication between various software components within a robot’s system.

The article discusses the benefits and challenges of combining Blender and Nvidia Isaac Sim to create realistic simulations for robotics applications. By integrating Blender’s 3D modeling and animation capabilities with Nvidia Isaac Sim’s simulation software, more detailed and accurate models can be developed for testing and refining algorithms. Additionally, synthetic data generated from Nvidia Isaac Sim can be used to train deep perception models, which reduces the time and effort required to gather and annotate datasets.

This blog is the second part of a two-part series where we will introduce the usage of sensor fusion in Autonomous Mobile Robots (AMRs) that are enabled to work with ROS (Robot Operating System) 2. In the first part, we introduced sensor fusion, briefly covered a sensor fusion algorithm called EKF (Extended Kalman Filters), and then walked through some of the experiments we did in simulation and on our AMR.

This blog is the first part of a two-part series in which we will introduce the usage of sensor fusion in Autonomous Mobile Robots (AMRs) that are enabled to work with ROS 2 (Robot Operating System 2). In this part, we will introduce sensor fusion, briefly cover a sensor fusion algorithm called EKF (Extended Kalman Filters), and then walk through some of the experiments we did in simulation and on our AMR.

Robotic Process Automation (RPA) has emerged as a winner for the tech industry after the pandemic hit the world. It not only became a household name for the tech industry, but at the same time, it was the only technology that paved its way in the forward direction since the pandemic. We have been reading about it on and on, and one thing is for sure, it makes human-oriented business tasks more straightforward, quicker, and error-free.

Earlier, humans managed every aspect of the business, even if that task was repetitive, rule-based, and time-consuming. Since humans are limited in their capacity of not only how long they can work at a stretch, and at what speed, but this is also affected by how motivated they are. Lower motivation, slower output; and repetitive tasks are certainly not good for motivation. As a result, the pace at which businesses could grow was limited.