Sensor Fusion Engineer Intern at Endiatx
Wayne State University
Overview Duration: 3-month internship with potential for full-time conversion Endiatx is a pioneering medical device company based in Hayward, California, developing cutting-edge microrobotics for diagnostic applications in the human body. Our flagship product, PillBot™, is a propeller-driven endoscopic capsule designed to transform gastrointestinal screening. We are a multidisciplinary team of innovators, engineers, clinicians, and designers working together to change the future of medical diagnostics. Position Summary Endiatx is seeking a detail-oriented and innovative Sensor Fusion Engineer Intern to join our team for the summer of 2025. This internship is ideal for an undergraduate or graduate student with a strong foundation in robotics, computer vision, estimation theory, and signal processing. You will be responsible for developing algorithms to combine data from the PillBot™'s onboard Inertial Measurement Unit (IMU) and front-facing camera to produce a robust, drift-free estimate of the robot's position, velocity, and orientation within the stomach environment. Responsibilities Develop sensor fusion algorithms: Design and implement algorithms to combine IMU data (acceleration, angular velocity) with visual information from the capsule's camera. Implement state estimation techniques: Apply and adapt techniques such as Kalman filters (e.g., EKF, UKF) to estimate the robot's position and orientation. Incorporate visual data for drift correction: Utilize camera images for visual odometry or other methods to correct for IMU drift and enhance localization accuracy. Evaluate performance: Test and validate the sensor fusion system's accuracy and robustness using simulated and potentially real-world data. Collaborate with ML/CV team: Work with the Machine Learning/Computer Vision intern to ensure complementary approaches to robot localization and environmental understanding. Desired Qualifications Pursuing or completed a degree in Robotics, Electrical Engineering, Computer Science, or a related field. Strong academic background in estimation theory, signal processing, and control systems . Experience with Kalman filters (EKF, UKF) or similar probabilistic state estimation techniques. Familiarity with computer vision concepts and processing camera data for pose estimation (e.g., visual odometry). Proficiency in programming languages like Python or C++ . Analytical and problem-solving skills, with a focus on practical application in a robotics context. #J-18808-Ljbffr Wayne State University
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