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Localization is a fundamental task in robotics for autonomous navigation.Existing localization methods rely on a single input data modality or trainseveral computational models to process different modalities. This leads tostringent computational requirements and sub-optimal results that fail tocapitalize on the complementary information in other data streams. This paperproposes UnLoc, a novel unified neural modeling approach for localization withmulti-sensor input in all weather conditions. Our multi-stream network canhandle LiDAR, Camera and RADAR inputs for localization on demand, i.e., it canwork with one or more input sensors, making it robust to sensor failure. UnLocuses 3D sparse convolutions and cylindrical partitioning of the space toprocess LiDAR frames and implements ResNet blocks with a slot attention-basedfeature filtering module for the Radar and image modalities. We introduce aunique learnable modality encoding scheme to distinguish between the inputsensor data. Our method is extensively evaluated on Oxford Radar RobotCar,ApolloSouthBay and Perth-WA datasets. The results ascertain the efficacy of ourtechnique.

UnLoc: A Universal Localization Method for Autonomous Vehicles using LiDAR, Radar and/or Camera Input