Swaayatt Robots is seeking motivated and passionate individuals eager to contribute to the cutting-edge fields of autonomous driving and AI. Our vision is to lead globally in these areas, and we aim to attract those who share our drive. Joining our team means working on groundbreaking projects that push technological boundaries and shape the future of transportation. We foster a collaborative environment where innovation and continuous learning are encouraged.
If you're excited to be at the forefront of transformative technologies and ready to take on challenges, join us on this journey. At Swaayatt Robots, you'll have the opportunity to make a real impact and help revolutionize autonomous driving and AI. Be part of our visionary team and help shape the future of these industries. Apply now for a rewarding career with us!
As a Mapping and Localization Engineer at Swaayatt Robots, you will be responsible for designing, developing and testing software for mapping and localizing autonomous vehicles in challenging and unstructured environments. The work includes building maps of various environments in a modular and efficient manner so that the autonomous vehicles can localize in such environments with very high precision.
Responsibilities:
- Implement and test existing state-of-the-art algorithms and pipelines for mapping and localization
- Design, develop, and test novel algorithmic pipelines for performing mapping and localization
- Work with data from various sensors and come up with techniques and solutions to solve the mapping and localization problem in challenging environments
- Work on calibrating various sensors for integrating and on sensor fusion
- Write high quality, efficient and modular code
Requirements:
- Strong C/C++ and Python programming experience
- Strong knowledge of data structures and algorithms
- Familiarity with Bash Scripting, Build Systems, Version Control
- Strong mathematical knowledge (Probability, Calculus, Linear Algebra, and various filters, like Kalman, Particle, EKF)
- Knowledge of probabilistic graphical models, and numerical optimization techniques
- Strong knowledge and experience with ROS and LCM or ZCM
- Strong Knowledge of, and experience working with, real-time non-linear kalman filtering and smoothing techniques
- Strong knowledge of, and experience with, 3D coordinate frames, transformations, multiple view geometry, and knowledge about lie groups and lie algebra.
- Strong knowledge of pose-graph optimization, and experience with pose-graph optimization libraries like G2O/Ceres/GTSAM
Bonus qualification:
- Knowledge of computer vision and control systems.
- Experience working and interfacing with various sensors like Cameras, LIDARs, GPS, IMU etc.
- Knowledge in machine learning and deep learning.
- Experience with real-time operating systems (like FreeRTOS).
