Localization

Mapping/SLAM (JMI) and Localization (WAI) are key ingredients for autonomous vehicles to perform any task in real-world operations. Creating a map (SLAM) of the environment requires a skillset to get a global position similar to what a GPS provides. The technology works without any structural support or changing the operating area. Mapping large areas can be achieved in a few steps, and the localization outputs stable high accuracy results even in challenging situations. As localization depends on a well-calibrated sensor setup and meets client expectations of accuracy and speed, we have set a minimum configuration for specific tasks. The modular software supports a wide range of standard sensors and combinations.

Tasks: Indoor/Outdoor Localization in 2D (x, y, theta) or 3D (x, y, z, yaw, pitch, roll)

Sensors: LIDAR (Ouster, Velodyne, SICK, …), Depth Camera (Intel Realsense, Azure Kinect, …), Inertial Measurement Units

SLAM stands for Simultaneous Localization and Mapping. It is a method of creating a map which finds its applications in robotics, autonomous vehicles, augmented reality and many other related fields.

At its core, SLAM is designed with the overarching goal of generating a comprehensive map of an unfamiliar environment, while concurrently tracking the precise location of a robot within that same environment. This dual functionality is essential in enabling autonomous systems to navigate unfamiliar surroundings with a high degree of accuracy.

Successfull implementation of SLAM, involves careful consideration of multiple factors such as:

• Calibration of sensors

• Fusion of data from different sensors

• Managment and efficiency of real time computational demands

• Accurate localization

• Consideration of dynamic changes

Achieving accuracy and reliability in SLAM requires an approach that addresses all challenges, ensuring effective mapping and localization in real-world applications.