Panoramic cameras offer a 4π steradian field of view, which is desirable for tasks like people detection and tracking since nobody can exit the field of view. Despite the recent diffusion of low-cost panoramic cameras, their usage in robotics remains constrained by the limited availability of datasets featuring annotations in the robot space, including people’s 2D or 3D positions. To tackle this issue, we introduce PanNote, an automatic annotation tool for people’s positions in panoramic videos. Our tool is designed to be cost-effective and straightforward to use without requiring human intervention during the labeling process and enabling the training of machine learning models with low effort. The proposed method introduces a calibration model and a data association algorithm to fuse data from panoramic images and 2D LiDAR readings. We validate the capabilities of PanNote by collecting a real-world dataset. On these data, we compared manual labels, automatic labels and the predictions of a baseline deep neural network. Results clearly show the advantage of using our method, with a 15-fold speed up in labeling time and a considerable gain in performance while training deep neural models on automatically labelled data.

In SLAM (Simultaneous localization and mapping) problems, Pose Graph Optimization (PGO) is a technique to refine an initial estimate of a set of poses (positions and orientations) from a set of pairwise relative measurements. The optimization procedure can be negatively affected even by a single outlier measurement, with possible catastrophic and meaningless results. Although recent works on robust optimization aim to mitigate the presence of outlier measurements, robust solutions capable of handling large numbers of outliers are yet to come. This paper presents IPC, acronym for Incremental Probabilistic Consensus, a method that approximates the solution to the combinatorial problem of finding the maximally consistent set of measurements in an incremental fashion. It evaluates the consistency of each loop closure measurement through a consensus-based procedure, possibly applied to a subset of the global problem, where all previously integrated inlier measurements have veto power. We evaluated IPC on standard benchmarks against several state-of-the-art methods. Although it is simple and relatively easy to implement, IPC competes with or outperforms the other tested methods in handling outliers while providing online performances. We release with this paper an open-source implementation of the proposed method.

Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non-linear optimization problem, what instead is rarely done is to exploit the underlying graph structure of the problem itself. Actually, the problem of hand-eye calibration can be seen as an instance of the Simultaneous Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we present a pose-graph approach to the hand-eye calibration problem that extends a recent state-of-the-art solution in two different ways: i) by formulating the solution to eye-on-base setups with one camera; ii) by covering multi-camera robotic setups. The proposed approach has been validated in simulation against standard hand-eye calibration methods. Moreover, a real application is shown. In both scenarios, the proposed approach overcomes all alternative methods. We release with this paper an open-source implementation of our graph-based optimization framework for multi-camera setups.

Self-driving vehicles and autonomous ground robots require a reliable and accurate method to analyze the traversability of the surrounding environment for safe navigation. This paper proposes a hybrid approach that combines geometric and appearance features for training Deep Encoder-Decoder architectures to detect the traversability score in real urban contexts. The proposed approach has been tested with two Deep Learning architectures on a public dataset of outdoor driving scenarios. Thanks to our approach, we are able to reach high levels of accuracy in detecting the correct traversability score in environments of highly variable complexity. This demonstrates the effectiveness and robustness of the proposed method.