Spot-On Dead Reckoning for Indoor Autonomous Robots

Dead reckoning navigation is a critical component of consumer and industrial robots. Dead reckoning is used in Camera and Lidar SLAM systems to help track location and maintain user experience in challenging environments such as featureless areas. With sufficient accuracy and robustness, dead reckoning can serve as the primary navigation paradigm, enabling a new class of lower-cost intelligent bots.

Typical sensors used in dead reckoning applications are wheel encoders, IMUs, and optical flow sensors. But these sensors can be severely impacted by the environment: wheels can slip or skid, optical flow measurements can vary on challenging surfaces, and IMUs are influenced by temperature changes and drift. 

CEVA has developed a unique solution that fuses measurements from these three sensors to achieve significantly better accuracy and robustness. Join our live webinar to learn how CEVA uses its expertise in sensor analytics, calibration, and sensor fusion improving accuracy by a factor of 5x in challenging conditions.

CEVA’s solution works with a variety of consumer MEMS sensors, wheel encoder technologies and has partnered with PixArt for optical flow sensing. PixArt’s PAA5101 is a high performance and high accuracy optical tracking chip using a unique LASER/LED light source that enables better tracking on both hard and soft surfaces.

Join CEVA and PixArt experts to learn about:

• The importance of great dead reckoning for autonomous robots
• Obstacles to accurate and robust dead reckoning
• How dynamic calibration and sensor fusion can dramatically improve accuracy and robustness
• Verifying performance in the real world
• More about the MotionEngine Scout Software and the PAA5101 sensor and their capabilities

Design engineers and product managers looking to understand how to improve the accuracy and robustness of dead reckoning in their products using CEVA’s MotionEngine Scout. The audience will also learn how this level of dead reckoning can create opportunities for lower cost roving robots.