Tasks:

• Develop and implement SLAM algorithms for accurate simultaneous localization and

mapping of autonomous robots in outdoor environments..

• Integrate sensor data from various sources (lidar, cameras, IMUs, wheel encoders) to

improve SLAM performance and accuracy.

• Utilize calibration techniques to ensure accurate sensor data fusion for SLAM.

• Collaborate with other team members to ensure overall system integration and

functionality.


Education:

• A bachelor's or master's degree in Robotics, Computer Science, Electrical

Engineering, or a related field is typically required.


Technical Skills:

• SLAM algorithms: Proficiency in developing and implementing SLAM

techniques such as EKF-SLAM, Graph SLAM, or Fast SLAM.

• Sensor fusion: Experience with integrating data from sensors such as lidar,

cameras, IMUs, and wheel encoders for accurate localization and mapping.

• Robotics: In-depth understanding of robotic systems, kinematics, dynamics,

and control theory.

• Programming languages: Proficiency in languages commonly used in

robotics, such as C++, Python, or ROS (Robot Operating System).

• Simulation tools: Experience with robotic simulation frameworks like Gazebo

or V-REP.

• Problem-solving and analytical skills: Strong analytical and problem-solving

abilities are vital for identifying issues, debugging complex systems, and
proposing effective solutions.

• Communication and teamwork: Excellent verbal and written communication

skills to collaborate with cross-functional teams, document technical
specifications, and present findings effectively.


Experience:

• SLAM implementation: Prior experience in developing SLAM algorithms and

systems for autonomous robots.

• Sensor integration: Knowledge of integrating sensor data from various

sources to improve SLAM performance.

• ROS: Practical experience with the Robot Operating System (ROS) for robotic

software development and integration.