93 citations · 295 across the 16 of their papers we have counts for
16 papers
UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation
Rohit Mohan, Florian Drews, Yakov Miron +2
LiDAR-camera fusion enhances 3D panoptic segmentation by leveraging camera images to complement sparse LiDAR scans, but it also introduces a critical failure mode. Under adverse co…
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…
RaLF: Flow-based Global and Metric Radar Localization in LiDAR Maps
Abhijeet Nayak, Daniele Cattaneo, Abhinav Valada
Localization is paramount for autonomous robots. While camera and LiDAR-based approaches have been extensively investigated, they are affected by adverse illumination and weather c…
Syn-Mediverse: A Multimodal Synthetic Dataset for Intelligent Scene Understanding of Healthcare Facilities
Rohit Mohan, José Arce, Sassan Mokhtar +2
Safety and efficiency are paramount in healthcare facilities where the lives of patients are at stake. Despite the adoption of robots to assist medical staff in challenging tasks s…
CoVIO: Online Continual Learning for Visual-Inertial Odometry
Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard +1
Visual odometry is a fundamental task for many applications on mobile devices and robotic platforms. Since such applications are oftentimes not limited to predefined target domains…
Array-Aware Matching: Taming the Complexity of Large-Scale Simulation Models
Massimo Fioravanti, Daniele Cattaneo, Federico Terraneo +5
Equation-based modelling is a powerful approach to tame the complexity of large-scale simulation problems. Equation-based tools automatically translate models into imperative langu…
PADLoC: LiDAR-Based Deep Loop Closure Detection and Registration Using Panoptic Attention
José Arce, Niclas Vödisch, Daniele Cattaneo +2
A key component of graph-based SLAM systems is the ability to detect loop closures in a trajectory to reduce the drift accumulated over time from the odometry. Most LiDAR-based met…
Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping Through Continual Learning
Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard +1
Robots operating in the open world encounter various different environments that can substantially differ from each other. This domain gap also poses a challenge for Simultaneous L…
Unsupervised Domain Adaptation for LiDAR Panoptic Segmentation
Borna Bešić, Nikhil Gosala, Daniele Cattaneo +1
Scene understanding is a pivotal task for autonomous vehicles to safely navigate in the environment. Recent advances in deep learning enable accurate semantic reconstruction of the…
LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM
Daniele Cattaneo, Matteo Vaghi, Abhinav Valada
Loop closure detection is an essential component of Simultaneous Localization and Mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several dee…
CMRNet++: Map and Camera Agnostic Monocular Visual Localization in LiDAR Maps
Daniele Cattaneo, Domenico Giorgio Sorrenti, Abhinav Valada
Localization is a critically essential and crucial enabler of autonomous robots. While deep learning has made significant strides in many computer vision tasks, it is still yet to…
A Benchmark for Point Clouds Registration Algorithms
Simone Fontana, Daniele Cattaneo, Augusto Luis Ballardini +2
Point clouds registration is a fundamental step of many point clouds processing pipelines; however, most algorithms are tested on data that are collected ad-hoc and not shared with…
Vehicle Ego-Lane Estimation with Sensor Failure Modeling
Augusto Luis Ballardini, Daniele Cattaneo, Rubén Izquierdo +4
We present a probabilistic ego-lane estimation algorithm for highway-like scenarios that is designed to increase the accuracy of the ego-lane estimate, which can be obtained relyin…
Global visual localization in LiDAR-maps through shared 2D-3D embedding space
Daniele Cattaneo, Matteo Vaghi, Simone Fontana +2
Global localization is an important and widely studied problem for many robotic applications. Place recognition approaches can be exploited to solve this task, e.g., in the autonom…
CMRNet: Camera to LiDAR-Map Registration
Daniele Cattaneo, Matteo Vaghi, Augusto Luis Ballardini +3
In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network to localize an RGB image of a scene in a map built from LiDAR data. Our network is not…
A dataset for benchmarking vision-based localization at intersections
Augusto L. Ballardini, Daniele Cattaneo, Domenico G. Sorrenti
In this report we present the work performed in order to build a dataset for benchmarking vision-based localization at intersections, i.e., a set of stereo video sequences taken fr…