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MOZARD: Multi-Modal Localization for Autonomous Vehicles in Urban Outdoor Environments
(2020)2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)Visually poor scenarios are one of the main sources of failure in visual localization systems in outdoor environments. To address this challenge, we present MOZARD, a multi-modal localization system for urban outdoor environments using vision and LiDAR. By fusing key point based visual multi-session information with semantic data, an improved localization recall can be achieved across vastly different appearance conditions. In particular ...Conference Paper -
Fishyscapes: A Benchmark for Safe Semantic Segmentation in Autonomous Driving
(2020)2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)Conference Paper -
Learning Camera Miscalibration Detection
(2020)2020 IEEE International Conference on Robotics and Automation (ICRA)Self-diagnosis and self-repair are some of the key challenges in deploying robotic platforms for long-term real-world applications. One of the issues that can occur to a robot is miscalibration of its sensors due to aging, environmental transients, or external disturbances. Precise calibration lies at the core of a variety of applications, due to the need to accurately perceive the world. However, while a lot of work has focused on ...Conference Paper