229 citations · 1k across the 84 of their papers we have counts for
12 papers · 1 filter
Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations
Michael Kurenkov, Sajad Marvi, Julian Schmidt +6
The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is cruc…
Progressive Multi-Modal Fusion for Robust 3D Object Detection
Rohit Mohan, Daniele Cattaneo, Florian Drews +1
Multi-sensor fusion is crucial for accurate 3D object detection in autonomous driving, with cameras and LiDAR being the most commonly used sensors. However, existing methods perfor…
Panoptic-Depth Forecasting
Juana Valeria Hurtado, Riya Mohan, Abhinav Valada
Forecasting the semantics and 3D structure of scenes is essential for robots to navigate and plan actions safely. Recent methods have explored semantic and panoptic scene forecasti…
Taxonomy-Aware Continual Semantic Segmentation in Hyperbolic Spaces for Open-World Perception
Julia Hindel, Daniele Cattaneo, Abhinav Valada
Semantic segmentation models are typically trained on a fixed set of classes, limiting their applicability in open-world scenarios. Class-incremental semantic segmentation aims to…
LetsMap: Unsupervised Representation Learning for Semantic BEV Mapping
Nikhil Gosala, Kürsat Petek, B Ravi Kiran +4
Semantic Bird's Eye View (BEV) maps offer a rich representation with strong occlusion reasoning for various decision making tasks in autonomous driving. However, most BEV mapping a…
A Good Foundation is Worth Many Labels: Label-Efficient Panoptic Segmentation
Niclas Vödisch, Kürsat Petek, Markus Käppeler +2
A key challenge for the widespread application of learning-based models for robotic perception is to significantly reduce the required amount of annotated training data while achie…
A Point-Based Approach to Efficient LiDAR Multi-Task Perception
Christopher Lang, Alexander Braun, Lars Schillingmann +1
Multi-task networks can potentially improve performance and computational efficiency compared to single-task networks, facilitating online deployment. However, current multi-task a…
Automatic Target-Less Camera-LiDAR Calibration From Motion and Deep Point Correspondences
Kürsat Petek, Niclas Vödisch, Johannes Meyer +3
Sensor setups of robotic platforms commonly include both camera and LiDAR as they provide complementary information. However, fusing these two modalities typically requires a highl…
BEVCar: Camera-Radar Fusion for BEV Map and Object Segmentation
Jonas Schramm, Niclas Vödisch, Kürsat Petek +4
Semantic scene segmentation from a bird's-eye-view (BEV) perspective plays a crucial role in facilitating planning and decision-making for mobile robots. Although recent vision-onl…
Evaluation of a Smart Mobile Robotic System for Industrial Plant Inspection and Supervision
Georg K. J. Fischer, Max Bergau, D. Adriana Gómez-Rosal +10
Automated and autonomous industrial inspection is a longstanding research field, driven by the necessity to enhance safety and efficiency within industrial settings. In addressing…
CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration
Daniele Cattaneo, Abhinav Valada
LiDARs are widely used for mapping and localization in dynamic environments. However, their high cost limits their widespread adoption. On the other hand, monocular localization in…
Semantic Scene Segmentation for Robotics
Juana Valeria Hurtado, Abhinav Valada
Comprehensive scene understanding is a critical enabler of robot autonomy. Semantic segmentation is one of the key scene understanding tasks which is pivotal for several robotics a…