Fast and Robust Detection of Fallen People from a Mobile Robot

This paper deals with the problem of detecting fallen people lying on the floor by means of a mobile robot equipped with a 3D depth sensor. In the proposed algorithm, inspired by semantic segmentation techniques, the 3D scene is over-segmented into small patches. Fallen people are then detected by means of two SVM classifiers: the first one labels each patch, while the second one captures the spatial relations between them. This novel approach showed to be robust and fast. Indeed, thanks to the use of small patches, fallen people in real cluttered scenes with objects side by side are correctly detected. Moreover, the algorithm can be executed on a mobile robot fitted with a standard laptop making it possible to exploit the 2D environmental map built by the robot and the multiple points of view obtained during the robot navigation. Additionally, this algorithm is robust to illumination changes since it does not rely on RGB data but on depth data. All the methods have been thoroughly validated on the IASLAB-RGBD Fallen Person Dataset, which is published online as a further contribution. It consists of several static and dynamic sequences with 15 different people and 2 different environments

Robust Digital-Twin Localization via An RGBD-based Transformer Network and A Comprehensive Evaluation on a Mobile Dataset

The potential of digital-twin technology, involving the creation of precise digital replicas of physical objects, to reshape AR experiences in 3D object tracking and localization scenarios is significant. However, enabling robust 3D object tracking in dynamic mobile AR environments remains a formidable challenge. These scenarios often require a more robust pose estimator capable of handling the inherent sensor-level measurement noise. In this paper, recognizing the challenges of comprehensive solutions in existing literature, we propose a transformer-based 6DoF pose estimator designed to achieve state-of-the-art accuracy under real-world noisy data. To systematically validate the new solution's performance against the prior art, we also introduce a novel RGBD dataset called Digital Twin Tracking Dataset v2 (DTTD2), which is focused on digital-twin object tracking scenarios. Expanded from an existing DTTD v1 (DTTD1), the new dataset adds digital-twin data captured using a cutting-edge mobile RGBD sensor suite on Apple iPhone 14 Pro, expanding the applicability of our approach to iPhone sensor data. Through extensive experimentation and in-depth analysis, we illustrate the effectiveness of our methods under significant depth data errors, surpassing the performance of existing baselines. Code and dataset are made publicly available at: https://github.com/augcog/DTTD

Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data

Localization is a key requirement for mobile robot autonomy and human-robot interaction. Vision-based localization is accurate and flexible, however, it incurs a high computational burden which limits its application on many resource-constrained platforms. In this paper, we address the problem of performing real-time localization in large-scale 3D point cloud maps of ever-growing size. While most systems using multi-modal information reduce localization time by employing side-channel information in a coarse manner (eg. WiFi for a rough prior position estimate), we propose to inter-weave the map with rich sensory data. This multi-modal approach achieves two key goals simultaneously. First, it enables us to harness additional sensory data to localise against a map covering a vast area in real-time; and secondly, it also allows us to roughly localise devices which are not equipped with a camera. The key to our approach is a localization policy based on a sequential Monte Carlo estimator. The localiser uses this policy to attempt point-matching only in nodes where it is likely to succeed, significantly increasing the efficiency of the localization process. The proposed multi-modal localization system is evaluated extensively in a large museum building. The results show that our multi-modal approach not only increases the localization accuracy but significantly reduces computational time.Comment: Presented at IEEE-RAS International Conference on Humanoid Robots (Humanoids) 201