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Achieve Autonomous Navigation with SLAM and Nav2

In the world of robotics, achieving autonomous navigation is a significant milestone. The ability of a robot to navigate its surroundings without human intervention opens up a wide range of possibilities and applications. One crucial aspect of autonomous navigation is accurate localization and mapping of the environment. Without this capability, a robot would be unable to understand its position or create an understanding of the surrounding space.

Accurate localization and mapping are essential for several reasons. Firstly, it allows the robot to know its exact position in relation to its starting point. This information is crucial for effective navigation as it provides a reference point for all subsequent movements. Secondly, creating a map of the environment enables the robot to have a comprehensive understanding of its surroundings. By building a detailed representation of obstacles, landmarks, and other relevant features, the robot can make informed decisions about where to move and how to avoid potential obstacles.

SLAM is an algorithmic approach that allows a robot to create a map of its environment while simultaneously localizing itself within that map. This technique has become widely used in robotics due to its effectiveness in addressing the challenges posed by simultaneous localization and mapping.

SLAM works by using sensor data, such as wheel odometry feedback and laser scans from lidar sensors, to estimate both the location of the robot within the map and construct an accurate representation of the surrounding environment. By combining these two processes into one algorithmic framework, SLAM enables robots to navigate autonomously with precision.

In this blog post, we will provide you with a step-by-step guide on how to achieve autonomous navigation using SLAM and Nav2 (Navigation 2). We will walk you through each step necessary for implementing SLAM and Nav2 effectively.

Step1: Obtaining Wheel Odometry Feedback

To achieve accurate localization and mapping for autonomous navigation, one of the crucial steps is obtaining proper wheel odometry feedback. Wheel odometry refers to the process of estimating the position and velocity of a robot based on the rotation of its wheels. This information is essential for determining the actual position of the robot with respect to its initial position when it powers on.

In our case, we can get profit from the ODrive motor driver which provides position and velocity feedback based on the motor hall sensor. The ODrive controller is specifically designed for robotics applications and offers precise control over motor movements.

To summarize, obtaining proper wheel odometry feedback is crucial for achieving accurate localization and mapping in autonomous navigation.

Step2: Testing and Preparing the Lidar

The lidar plays a crucial role in achieving accurate and reliable autonomous navigation for the SARA robot. Lidar, which stands for Light Detection and Ranging, is a remote sensing technology that uses laser beams to measure distances and create detailed maps of the surrounding environment. By emitting laser pulses and measuring the time it takes for them to bounce back, the lidar sensor can accurately determine the distance to objects in its field of view.

To ensure that the lidar is functioning properly, it is important to conduct thorough testing and prepare it accordingly.

Once the physical setup is confirmed, it is necessary to test the lidar's functionality. This can be done by running the lidar node package available for ROS2 and trying to get data from it. These tests typically involve rotating the lidar sensor and observing its ability to detect objects at different distances and angles. It is important to verify that the sensor can accurately measure distances and provide reliable data.

By thoroughly testing and preparing the lidar sensor before integration into the SARA robot's system, you can ensure its proper functionality during autonomous navigation tasks. A well-calibrated and properly configured lidar will provide accurate measurements of the surrounding environment, enabling SLAM algorithms to create detailed maps and the navigation stack to effectively guide the robot through obstacles.

Step3: Implementing SLAM

SLAM, or Simultaneous Localization And Mapping, is a crucial technique in achieving accurate localization and mapping for autonomous navigation. It allows the robot to create a map of its environment while simultaneously determining its own position within that map. By implementing SLAM, the SARA robot can navigate through unfamiliar spaces with precision and efficiency.

After ensuring that both wheel odometry feedback and lidar functionality are in place, we can proceed with implementing SLAM for mapping. This involves utilizing algorithms that combine sensor data from various sources to create an accurate representation of the environment. These algorithms take into account factors the laser scan data from the lidar, the robot's movement from wheel odometry feedback, and other sensor inputs.

During this process, SLAM creates a map by identifying landmarks or key features in the environment and determining their relative positions to each other. It also estimates the robot's position within this map by comparing sensor data with previously collected information. By continuously updating this map as it navigates through different areas, SARA can build a comprehensive understanding of its surroundings.

The final step in achieving autonomous navigation through SLAM is to utilize the navigation stack. This involves preparing the appropriate launch file and tuning the Nav2 parameters to suit the robot's specific requirements. The navigation stack uses the map generated by SLAM to plan and execute autonomous movements, avoiding obstacles and reaching desired destinations.

Step4: Utilizing the Navigation Stack for Autonomous Navigation

The navigation stack plays a crucial role in achieving autonomous navigation for the SARA robot. It is a collection of software components that work together to enable the robot to move from one location to another while avoiding obstacles and following a desired path. In this section, we will provide an overview of the navigation stack and explain how it can be utilized to achieve autonomous navigation.

The navigation stack consists of several modules, including localization, mapping, path planning, and control. These modules work together seamlessly to enable the robot to navigate autonomously in its environment. The first step in utilizing the navigation stack is to ensure accurate localization of the robot within the map created by SLAM.

Localization is the process of determining the robot's position and orientation relative to its surroundings. It allows the robot to know where it is in relation to its target destination and helps it make informed decisions about its movements. The navigation stack utilizes various localization algorithms, the most used one is the Adaptive Monte Carlo Localization (AMCL), to estimate the robot's pose based on sensor data.

Once localization is achieved, the next step is path planning. Path planning involves generating a collision-free trajectory from the current position of the robot to its desired destination.

After generating a path, the control module comes into play. This module takes care of executing the planned trajectory by sending appropriate commands to actuators such as motors or servos. It ensures that the robot follows the desired path accurately while taking into account factors like velocity constraints and obstacle avoidance.

To utilize the navigation stack effectively, it is necessary to configure and tune various parameters according to specific requirements. Parameters such as planner frequency, local costmap resolution, or global planner type can be adjusted depending on factors like robot dynamics or environment complexity.

In addition to configuration, launching the navigation stack also requires creating appropriate launch files. These launch files define the nodes, topics, and parameters that need to be initialized for successful navigation.


The implementation of SLAM and Nav2 in achieving autonomous navigation for the SARA robot is a significant step towards creating a smart and efficient robotic advertiser. By following the step-by-step guide outlined in this blog post, robotics enthusiasts, engineers, and developers can learn how to utilize these technologies effectively.

[21/July ~ 4/August 2023]

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