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 FastSLAM.
● 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.
