Planning

Application	Description	Reference
Waypoint Following	Within Nav2, waypoint following is a feature that allows a robot to autonomously navigate through a series of specified points (waypoints) in sequence. It leverages the navigation stack’s planning and control capabilities to ensure accurate and reliable traversal of a given route.	Doc
Path Planning	Path planning in Nav2 refers to the process where the navigation stack computes a feasible and efficient path from the robot’s current location to its target destination. This process considers the environment’s obstacles and robot’s kinematics, using built-in planning algorithms.	Doc

Visual Perception

Application	Description	Reference
Barcode Recognition	Basic ROS2 wrapper for the zbar barcode reader library. Reads image stream from image topic, and outputs detected barcodes to barcode topic. Works with 1D and 2D barcodes.	Github

Application	Description	Reference
IMU Tools	A set of utilities for processing and visualizing data from Inertial Measurement Units (IMUs), which provide orientation, acceleration, and angular velocity information.	Github

Application	Description	Reference
RTAB-Map	Real-Time Appearance-Based Mapping (RTAB-Map) is a graph-based SLAM (Simultaneous Localization and Mapping) algorithm that creates 3D maps using visual, depth, and sensor data.	Github
Cartographer	A real-time SLAM algorithm developed by Google that enables robots to build 2D and 3D maps of their environment using laser and odometry data.	Doc Github
SLAM Toolbox	A collection of SLAM algorithms and tools for mapping and localization, supporting online and offline map building, pose-graph optimization, and loop closure.	Github
LIO-SAM	Lidar-Inertial Odometry via Smoothing and Mapping (LIO-SAM) is a state-of-the-art SLAM system that fuses LiDAR and IMU data to produce accurate, high-frequency odometry and maps.	Github

Application	Description	Reference
Navigation 2	A ROS2 framework that provides a complete set of navigation features, including localization, path planning, and control, for autonomous robot movement in dynamic environments.	Doc Github
Robot Localization	A ROS2 package that fuses data from various sensors (e.g., GPS, IMU, odometry) to provide accurate state estimation of a robot’s position and orientation.	Doc Github
Hybrid A*	Hybrid A* is one of the path planning algorithms integrated in Nav2. It enhances traditional A* by considering the robot's motion constraints and continuous state space, making it suitable for robots that require smooth and realistic paths, especially non-holonomic vehicles.	Doc
Dijkstra	Dijkstra’s algorithm, available within Nav2’s planning plugins, is used to find the shortest path in a grid map from start to goal. It guarantees the optimal solution and serves as a robust global planner for route generation in the navigation stack.	Doc
Obstacle Avoidance	Obstacle avoidance in Nav2 is handled by its local planner and costmap modules. These components work together to detect obstacles in real time and dynamically adjust the robot’s path, ensuring safe navigation even in changing environments.	Doc