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SLAM (Simultaneous Localization and Mapping) is crucial for autonomous robotic systems, enabling efficient navigation and mapping in unknown environments.

While various SLAM frameworks cater to different sensor modalities, they all focus on two primary objectives: refining a robot's trajectory and simultaneously updating its map. Lidar sensors are particularly valued for their precision in high-speed applications like self-driving cars, providing accurate 2D or 3D point cloud data. Additionally, Inertial Measurement Units (IMUs) are often integrated to provide additional motion data, enhancing system robustness. Modern SLAM frameworks leverage lidar data using graph optimization techniques (e.g., LIO-SAM), filter-based methods (e.g., EKF-SLAM, FastSLAM, Point-LIO, FAST-LIO2), and feature-based methods (e.g., LOAM).

This beautiful example dataset was collected by Doncey Albin and team out of the Autonomous Robotics and Perception Group (ARPG) at The University of Colorado Boulder, using an AgileX Robotics Hunter SE equipped with a 64-beam Ouster OS1 lidar, LORD Microstrain IMU, and CENet, trained on SemanticKITTI and modified into ROS1, used on the CU Boulder campus as a zero-shot method.

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