5 papers
Perception Matters: Enhancing Embodied AI with Uncertainty-Aware Semantic Segmentation
Sai Prasanna, Daniel Honerkamp, Kshitij Sirohi +3
Embodied AI has made significant progress acting in unexplored environments. However, tasks such as object search have largely focused on efficient policy learning. In this work, w…
uPLAM: Robust Panoptic Localization and Mapping Leveraging Perception Uncertainties
Kshitij Sirohi, Daniel Büscher, Wolfram Burgard
The availability of a robust map-based localization system is essential for the operation of many autonomously navigating vehicles. Since uncertainty is an inevitable part of perce…
Uncertainty-aware LiDAR Panoptic Segmentation
Kshitij Sirohi, Sajad Marvi, Daniel Büscher +1
Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Current lear…
Robust Monocular Localization in Sparse HD Maps Leveraging Multi-Task Uncertainty Estimation
Kürsat Petek, Kshitij Sirohi, Daniel Büscher +1
Robust localization in dense urban scenarios using a low-cost sensor setup and sparse HD maps is highly relevant for the current advances in autonomous driving, but remains a chall…
EfficientLPS: Efficient LiDAR Panoptic Segmentation
Kshitij Sirohi, Rohit Mohan, Daniel Büscher +2
Panoptic segmentation of point clouds is a crucial task that enables autonomous vehicles to comprehend their vicinity using their highly accurate and reliable LiDAR sensors. Existi…