Localization is a critical technology for various applications ranging from
navigation and surveillance to assisted living. Localization systems typically
fuse information from sensors viewing the scene from different perspectives to
estimate the target location while also employing multiple modalities for
enhanced robustness and accuracy. Recently, such systems have employed
end-to-end deep neural models trained on large datasets due to their superior
performance and ability to handle data from diverse sensor modalities. However,
such neural models are often trained on data collected from a particular set of
sensor poses (i.e., locations and orientations). During real-world deployments,
slight deviations from these sensor poses can result in extreme inaccuracies.
To address this challenge, we introduce FlexLoc, which employs conditional
neural networks to inject node perspective information to adapt the
localization pipeline. Specifically, a small subset of model weights are
derived from node poses at run time, enabling accurate generalization to unseen
perspectives with minimal additional overhead. Our evaluations on a multimodal,
multiview indoor tracking dataset showcase that FlexLoc improves the
localization accuracy by almost 50% in the zero-shot case (no calibration data
available) compared to the baselines. The source code of FlexLoc is available
at this https URL

采用条件神经网络将节点视角信息注入定位管道以适应于未见的视角，显著提高多视图室内跟踪中定位准确性的 FlexLoc 技术。