  Localization is a fundamental task in robotics for autonomous navigation.
Existing localization methods rely on a single input data modality or train
several computational models to process different modalities. This leads to
stringent computational requirements and sub-optimal results that fail to
capitalize on the complementary information in other data streams. This paper
proposes UnLoc, a novel unified neural modeling approach for localization with
multi-sensor input in all weather conditions. Our multi-stream network can
handle LiDAR, Camera and RADAR inputs for localization on demand, i.e., it can
work with one or more input sensors, making it robust to sensor failure. UnLoc
uses 3D sparse convolutions and cylindrical partitioning of the space to
process LiDAR frames and implements ResNet blocks with a slot attention-based
feature filtering module for the Radar and image modalities. We introduce a
unique learnable modality encoding scheme to distinguish between the input
sensor data. Our method is extensively evaluated on Oxford Radar RobotCar,
ApolloSouthBay and Perth-WA datasets. The results ascertain the efficacy of our
technique.
