arXiv subjects

Michele Antonazzi

Publications and source records attributed to Michele Antonazzi.

7 recordsLinked to original sources

arXiv2026

Probabilistic Scene Graphs: Hierarchical Representation and Real-time System

3D scene graphs provide semantically rich and hierarchical representations for robot perception. However, existing systems do not maintain uncertainty as an explicit belief or propagate it through the operations that construct and refine the graph. We introduce Probabilistic Scene Graph (PSG), a generalization of the conventional scene graph that represents a posterior over possible graphs, factorized into a discrete graph structure of entities, relations, and semantic attributes, and continuous states that ground them spatially, with uncertainty maintained over both components. Geometry is carried directly by the nodes rather than selected from a separately constructed metric map, so a metric map, where needed, follows from the graph rather than preceding it. We instantiate PSG's probabilistic spatial grounding with hierarchical graphs of Gaussians (HGG): each object primitive is represented by a full-covariance Gaussian under a Normal-Inverse-Wishart belief, and the same parametrization applied recursively within a node yields a geometry graph that resolves its surface at finer resolution. We then build a mapping pipeline that preserves these beliefs throughout graph construction and refinement: a purely graph-based coarse-to-fine alignment registers observations by comparing node beliefs, while a nested Expectation-Maximization and factor-graph optimization jointly refines poses, object parameters, and internal geometry. Across six datasets spanning indoor RGB-D, outdoor LiDAR, and cross-modality deployment, HGG operates at sensor rate with near-constant memory and achieves state-of-the-art object accuracy and zero-shot graph alignment.

cs.RO↗

arXiv2026

Self-supervised Domain Adaptation for Visual 3D Pose Estimation of Nano-drone Racing Gates by Enforcing Geometric Consistency

We consider the task of visually estimating the relative pose of a drone racing gate in front of a nano-quadrotor, using a convolutional neural network pre-trained on simulated data to regress the gate's pose. Due to the sim-to-real gap, the pre-trained model underperforms in the real world and must be adapted to the target domain. We propose an unsupervised domain adaptation (UDA) approach using only real image sequences collected by the drone flying an arbitrary trajectory in front of a gate; sequences are annotated in a self-supervised fashion with the drone's odometry as measured by its onboard sensors. On this dataset, a state consistency loss enforces that two images acquired at different times yield pose predictions that are consistent with the drone's odometry. Results indicate that our approach outperforms other SoA UDA approaches, has a low mean absolute error in position (x=26, y=28, z=10 cm) and orientation ($ψ$=13${^{\circ}}$), an improvement of 40% in position and 37% in orientation over a baseline. The approach's effectiveness is appreciable with as few as 10 minutes of real-world flight data and yields models with an inference time of 30.4ms (33 fps) when deployed aboard the Crazyflie 2.1 Brushless nano-drone.

cs.RO↗

arXiv2026

Instance-Guided Unsupervised Domain Adaptation for Robotic Semantic Segmentation

Semantic segmentation networks, which are essential for robotic perception, often suffer from performance degradation when the visual distribution of the deployment environment differs from that of the source dataset on which they were trained. Unsupervised Domain Adaptation (UDA) addresses this challenge by adapting the network to the robot's target environment without external supervision, leveraging the large amounts of data a robot might naturally collect during long-term operation. In such settings, UDA methods can exploit multi-view consistency across the environment's map to fine-tune the model in an unsupervised fashion and mitigate domain shift. However, these approaches remain sensitive to cross-view instance-level inconsistencies. In this work, we propose a method that starts from a volumetric 3D map to generate multi-view consistent pseudo-labels. We then refine these labels using the zero-shot instance segmentation capabilities of a foundation model, enforcing instance-level coherence. The refined annotations serve as supervision for self-supervised fine-tuning, enabling the robot to adapt its perception system at deployment time. Experiments on real-world data demonstrate that our approach consistently improves performance over state-of-the-art UDA baselines based on multi-view consistency, without requiring any ground-truth labels in the target domain.

cs.RO↗

arXiv2025

Development and Adaptation of Robotic Vision in the Real-World: the Challenge of Door Detection

Mobile service robots are increasingly prevalent in human-centric, real-world domains, operating autonomously in unconstrained indoor environments. In such a context, robotic vision plays a central role in enabling service robots to perceive high-level environmental features from visual observations. Despite the data-driven approaches based on deep learning push the boundaries of vision systems, applying these techniques to real-world robotic scenarios presents unique methodological challenges. Traditional models fail to represent the challenging perception constraints typical of service robots and must be adapted for the specific environment where robots finally operate. We propose a method leveraging photorealistic simulations that balances data quality and acquisition costs for synthesizing visual datasets from the robot perspective used to train deep architectures. Then, we show the benefits in qualifying a general detector for the target domain in which the robot is deployed, showing also the trade-off between the effort for obtaining new examples from such a setting and the performance gain. In our extensive experimental campaign, we focus on the door detection task (namely recognizing the presence and the traversability of doorways) that, in dynamic settings, is useful to infer the topology of the map. Our findings are validated in a real-world robot deployment, comparing prominent deep-learning models and demonstrating the effectiveness of our approach in practical settings.

cs.RO↗

arXiv2024

R2SNet: Scalable Domain Adaptation for Object Detection in Cloud-Based Robotic Ecosystems via Proposal Refinement

We introduce a novel approach for scalable domain adaptation in cloud robotics scenarios where robots rely on third-party AI inference services powered by large pre-trained deep neural networks. Our method is based on a downstream proposal-refinement stage running locally on the robots, exploiting a new lightweight DNN architecture, R2SNet. This architecture aims to mitigate performance degradation from domain shifts by adapting the object detection process to the target environment, focusing on relabeling, rescoring, and suppression of bounding-box proposals. Our method allows for local execution on robots, addressing the scalability challenges of domain adaptation without incurring significant computational costs. Real-world results on mobile service robots performing door detection show the effectiveness of the proposed method in achieving scalable domain adaptation.

cs.RO↗

arXiv2024

Frontier-Based Exploration for Multi-Robot Rendezvous in Communication-Restricted Unknown Environments

Multi-robot rendezvous and exploration are fundamental challenges in the domain of mobile robotic systems. This paper addresses multi-robot rendezvous within an initially unknown environment where communication is only possible after the rendezvous. Traditionally, exploration has been focused on rapidly mapping the environment, often leading to suboptimal rendezvous performance in later stages. We adapt a standard frontier-based exploration technique to integrate exploration and rendezvous into a unified strategy, with a mechanism that allows robots to re-visit previously explored regions thus enhancing rendezvous opportunities. We validate our approach in 3D realistic simulations using ROS, showcasing its effectiveness in achieving faster rendezvous times compared to exploration strategies.

cs.RO↗

arXiv2023

Enhancing Door-Status Detection for Autonomous Mobile Robots during Environment-Specific Operational Use

Door-status detection, namely recognizing the presence of a door and its status (open or closed), can induce a remarkable impact on a mobile robot's navigation performance, especially for dynamic settings where doors can enable or disable passages, changing the topology of the map. In this work, we address the problem of building a door-status detector module for a mobile robot operating in the same environment for a long time, thus observing the same set of doors from different points of view. First, we show how to improve the mainstream approach based on object detection by considering the constrained perception setup typical of a mobile robot. Hence, we devise a method to build a dataset of images taken from a robot's perspective and we exploit it to obtain a door-status detector based on deep learning. We then leverage the typical working conditions of a robot to qualify the model for boosting its performance in the working environment via fine-tuning with additional data. Our experimental analysis shows the effectiveness of this method with results obtained both in simulation and in the real-world, that also highlight a trade-off between costs and benefits of the fine-tuning approach.

cs.RO↗