Research Associate (4D Radar Inertial Odometry)

As a Research Associate at the Centre for Advanced Robotics Technology Innovation (CARTIN) at NTU, your responsibilities will include designing and implementing real-time radar-inertial odometry algorithms, extracting ego-motion estimation from 4D radar and IMU data, and evaluating performance under challenging visual conditions such as fog and rain. You will also be responsible for integrating odometry outputs with downstream SLAM and mapping modules while collaborating closely with hardware and system integration teams.

Candidates applying for the Research Associate position at NTU's CARTIN must have a Master's degree in Robotics, Electrical Engineering, Computer Science, or related fields. Additionally, a strong background in 4D radar point cloud processing and sensor fusion is crucial, along with proficiency in C++ or Python and ROS1/2 implementation.

For the Research Associate role at NTU's CARTIN, candidates should have experience in point cloud-based odometry techniques such as LOAM, Fast-LIO, or LIO-SAM. Familiarity with real-world SLAM datasets and evaluation metrics is also important to effectively assess the performance of your developed algorithms in various conditions.

The Research Associate position at NTU's CARTIN plays a vital role by developing innovative radar-inertial fusion modules that facilitate robust ego-motion estimation in challenging environments. The research you conduct will enhance robotic applications, particularly in sectors where visibility is compromised, such as firefighting, thus pushing the boundaries of autonomous systems.

Candidates for the Research Associate role at NTU's CARTIN must possess programming expertise in C++ and Python. These skills are essential for implementing radar-inertial odometry algorithms and operating within the ROS1 and ROS2 frameworks, which are integral to the project's success in sensor fusion and SLAM.

When responding to this question, detail your specific experience with radar and IMU data fusion. Discuss any projects where you implemented algorithms for ego-motion estimation and how you used sensor fusion techniques to improve accuracy and reliability in adverse conditions.

Provide a concise overview of a project where you developed SLAM algorithms. Highlight the methodology you used, the challenges faced, and how you overcame them, particularly in terms of sensor integration and environmental obstacles.

Discuss specific metrics and methodologies you use for evaluating the performance of odometry systems. Mention benchmarks, real-world datasets, and how you interpret results to refine algorithms, demonstrating your familiarity with SLAM evaluation criteria.

Explain your approach to managing data from different sensors. Discuss synchronization processes, preprocessing steps, and how you address challenges like noise and drift to ensure effective data fusion and reliable motion tracking.

Share specific challenges you faced while processing radar data, such as noise or calibration issues, and detail your problem-solving strategies. This will demonstrate your critical thinking skills and technical expertise in radar technology.

In your answer, describe Kalman filtering's role in sensor fusion for merging data from multiple sources. Highlight its advantages in estimation accuracy and noise reduction, particularly in the context of radar and IMU integration for ego-motion estimation.

List the programming languages and frameworks you are skilled in, emphasizing your expertise in C++ and Python along with ROS1/2. Provide examples of how you've applied these languages in previous projects, particularly in robotics and SLAM.

Discuss the resources you use to stay informed about advancements in robotics and SLAM, such as academic journals, conferences, and online courses. Mention your commitment to continual learning and professional development in evolving tech fields.

Emphasize the importance of communication and collaboration in a multidisciplinary team. Share experiences where you worked alongside hardware engineers or software developers, and how you navigated differences in expertise to achieve common project goals.

In your response, express your passion for radar-inertial odometry and its potential applications in robotics. Discuss your motivation to tackle challenges in harsh environments and how this aligns with your career goals and interests.