14 May 2025

Autonomous Robotics Development in Robot Operating System (ROS) 2 Humble

Abstract: This report presents a novel Robot Operating System (ROS) 2–based simulation framework designed to facilitate the development and testing of an autonomous navigation stack. Elements of the navigation stack, including lidar odometry, simultaneous localization and mapping (SLAM), and frontier exploration, are discussed in detail. The key features of the navigation stack include real-time performance and scalable architecture. The simulation results were applied to a physical robot. As a result, the physical robot was able to autonomously map the interior of a building and to generate 2D occupancy and 3D point clouds of the environment.

14 May 2025

Robot Operating System Innovations in Autonomous Navigation

Abstract: This report presents the results of simulations conducted in preparation for the 2024 Maneuver Support and Protection Integration Experiments (MSPIX) demonstration. The study aimed to develop and test a system for autonomous navigation in complex environments using advanced algorithms to enable the robot to avoid obstacles and navigate safely and efficiently. The report describes the methodology used to develop and test the autonomous navigation system, including the use of simulation, to evaluate its performance. The results of the simulation tests are presented to highlight the effectiveness of the navigation solution.

7 Jan 2025

Exploring Lidar Odometry Within the Robot Operating System

Abstract: Here, we explore various lidar odometry approaches (with both 3 and 6 degrees of freedom) in simulation. We modified a virtual model of a TurtleBot3 robot to work with the various odometry approaches and evaluated each method within a gazebo simulation. The gazebo model was configured to generate an absolute ground truth for comparison to the odometry results. We used the evo package to compare the ground truth with the various lidar odometry values. The results for KISS-ICP and laser scan matcher (LSM), including two simultaneous localization and map-ping (SLAM) approaches, Fast Lidar-Inertial Odometry (FAST-LIO), and Direct Lidar Odometry (DLO), are provided and discussed. We also tested one of the approaches on our physical robot.

11 Sep 2024

Terrestrial Vision-Based Localization Using Synthetic Horizons

Abstract: Vision-based localization could improve navigation and routing solutions in GPS-denied environments. In this study, data from a Carnegie Robotics MultiSense S7 stereo camera were matched to a synthetic horizon derived from foundation sources using novel two-dimensional correlation techniques. Testing was conducted at multiple observation locations over known ground control points (GCPs) at the US Army Engineer Research and Development Center (ERDC), Geospatial Research Laboratory (GRL), Corbin Research Facility. Testing was conducted at several different observational azimuths for these locations to account for the many possible viewing angles in a scene. Multiple observational azimuths were also tested together to see how the amount of viewing angles affected results. These initial tests were conducted to help future efforts testing the S7 camera under more realistic conditions, in different environments, and while expanding the collection and processing methodologies to additional sensor systems.