In a cross-dataset generalization experiment, we show that our affordance learning scheme can be applied across a diverse mix of datasets and improves driveability estimation in unseen environments compared to general-purpose, single-dataset segmentation.
Reliable outdoor deployment of mobile robots requires the robust identification of permissible driving routes in a given environment.
With the rapidly growing expansion in the use of UAVs, the ability to autonomously navigate in varying environments and weather conditions remains a highly desirable but as-of-yet unsolved challenge.
Proactively perceiving others' intentions is a crucial skill to effectively interact in unstructured, dynamic and novel environments.
This paper is about alerting acoustic event detection and sound source localisation in an urban scenario.