Tasks:
â—Ź Develop and implement SLAM algorithms for accurate simultaneous localization and
mapping of autonomous robots in outdoor environments..
â—Ź Integrate sensor data from various sources (lidar, cameras, IMUs, wheel encoders) to
improve SLAM performance and accuracy.
â—Ź Utilize calibration techniques to ensure accurate sensor data fusion for SLAM.
â—Ź Collaborate with other team members to ensure overall system integration and
functionality.
Education:
â—Ź A bachelor's or master's degree in Robotics, Computer Science, Electrical
Engineering, or a related field is typically required.
Technical Skills:
â—Ź SLAM algorithms: Proficiency in developing and implementing SLAM
techniques such as EKF-SLAM, Graph SLAM, or FastSLAM.
â—Ź Sensor fusion: Experience with integrating data from sensors such as lidar,
cameras, IMUs, and wheel encoders for accurate localization and mapping.
â—Ź Robotics: In-depth understanding of robotic systems, kinematics, dynamics,
and control theory.
â—Ź Programming languages: Proficiency in languages commonly used in
robotics, such as C++, Python, or ROS (Robot Operating System).
â—Ź Simulation tools: Experience with robotic simulation frameworks like Gazebo
or V-REP.
â—Ź Problem-solving and analytical skills: Strong analytical and problem-solving
abilities are vital for identifying issues, debugging complex systems, and
proposing effective solutions.
â—Ź Communication and teamwork: Excellent verbal and written communication
skills to collaborate with cross-functional teams, document technical
specifications, and present findings effectively.
Experience:
â—Ź SLAM implementation: Prior experience in developing SLAM algorithms and
systems for autonomous robots.
â—Ź Sensor integration: Knowledge of integrating sensor data from various
sources to improve SLAM performance.
â—Ź ROS: Practical experience with the Robot Operating System (ROS) for robotic
software development and integration.