As autonomous driving programs strive to balance performance with production viability, there’s growing momentum around camera-first localization solutions. This session dives into vision-only approaches to localization — enabling vehicles to determine their position using camera data without dependency on dense HD maps or expensive sensor suites. We’ll explore how techniques like visual odometry, place recognition, and semantic cues are making localization more accessible and adaptable for real-world deployment.

• Visual odometry and ego-motion estimation in complex driving environments
• Leveraging semantic landmarks and visual cues for robust localization
• Strategies for localization in GNSS-degraded or map-sparse areas
• Fleet-scale benefits: camera-driven learning and scalable deployment