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3D VSG: Long-term Semantic Scene Change Prediction through 3D Variable Scene Graphs
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)Numerous applications require robots to operate in environments shared with other agents, such as humans or other robots. However, such shared scenes are typically subject to different kinds of long-term semantic scene changes. The ability to model and predict such changes is thus crucial for robot autonomy. In this work, we formalize the task of semantic scene variability estimation and identify three main varieties of semantic scene ...Conference Paper -
SphNet: A Spherical Network for Semantic Pointcloud Segmentation
(2023)2023 IEEE International Conference on Robotics and Automation (ICRA)Semantic segmentation for robotic systems can enable a wide range of applications, from self-driving cars and augmented reality systems to domestic robots. We argue that a spherical representation is a natural one for egocentric pointclouds. Thus, in this work, we present a novel framework exploiting such a representation of LiDAR pointclouds for the task of semantic segmentation. Our approach is based on a spherical convolutional neural ...Conference Paper -
Sampling-free obstacle gradients and reactive planning in Neural Radiance Fields
(2022)This work investigates the use of Neural implicit representations, specifically Neural Radiance Fields (NeRF), for geometrical queries and motion planning. We show that by adding the capacity to infer occupancy in a radius to a pre trained NeRF we are effectively learning an approximation to a Euclidean Signed Distance Field (ESDF). Even more, using backward differentiation of the network, we readily obtain the obstacle gradients that are ...Conference Paper -
Panoptic Multi-TSDFs: a Flexible Representation for Online Multi-resolution Volumetric Mapping and Long-term Dynamic Scene Consistency
(2022)2022 International Conference on Robotics and Automation (ICRA)For robotic interaction in environments shared with other agents, access to volumetric and semantic maps of the scene is crucial. However, such environments are inevitably subject to long-term changes, which the map needs to account for. We thus propose panoptic multi-TSDFs as a novel representation for multi-resolution volumetric mapping in changing environments. By leveraging high-level information for 3D reconstruction, our proposed ...Conference Paper -
See Yourself in Others: Attending Multiple Tasks for Own Failure Detection
(2022)2022 International Conference on Robotics and Automation (ICRA)Autonomous robots deal with unexpected scenarios in real environments. Given input images, various visual perception tasks can be performed, e.g., semantic segmentation, depth estimation and normal estimation. These different tasks provide rich information for the whole robotic perception system. All tasks have their own characteristics while sharing some latent correlations. However, some of the task predictions may suffer from the ...Conference Paper -
Collaborative Robot Mapping using Spectral Graph Analysis
(2022)2022 International Conference on Robotics and Automation (ICRA)In this paper, we deal with the problem of creating globally consistent pose graphs in a centralized multi-robot SLAM framework. For each robot to act autonomously, individual onboard pose estimates and maps are maintained, which are then communicated to a central server to build an optimized global map. However, inconsistencies between onboard and server estimates can occur due to onboard odometry drift or failure. Furthermore, robots ...Conference Paper -
Precise Robot Localization in Architectural 3D Plans
(2021)ISARC Proceedings ~ Proceedings of the 38th International Symposium on Automation and Robotics in Construction (ISARC)This paper presents a localization system for mobile robots enabling precise localization in inaccurate building models. The approach leverages local referencing to counteract inherent deviations between as-planned and as-built data for locally accurate registration. We further fuse a novel camera-based robust outlier detector with LiDAR data to reject a wide range of outlier measurements from clutter, dynamic objects, and sensor failures. ...Conference Paper -
Spherical Multi-Modal Place Recognition for Heterogeneous Sensor Systems
(2021)2021 IEEE International Conference on Robotics and Automation (ICRA)In this paper, we propose a robust end-to-end multi-modal pipeline for place recognition where the sensor systems can differ from the map building to the query. Our approach operates directly on images and LiDAR scans without requiring any local feature extraction modules. By projecting the sensor data onto the unit sphere, we learn a multi-modal descriptor of partially overlapping scenes using a spherical convolutional neural network. ...Conference Paper -
NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping
(2021)2021 International Conference on 3D Vision (3DV)We present a novel 3D mapping method leveraging the recent progress in neural implicit representation for 3D reconstruction. Most existing state-of-the-art neural implicit representation methods are limited to object-level reconstructions and can not incrementally perform updates given new data. In this work, we propose a fusion strategy and training pipeline to incrementally build and update neural implicit representations that enable ...Conference Paper -
Self-Improving Semantic Perception for Indoor Localisation
(2021)5th Annual Conference on Robot Learning (CoRL 2021)We propose a novel robotic system that can improve its semantic perception during deployment. Contrary to the established approach of learning semantics from large datasets and deploying fixed models, we propose a framework in which semantic models are continuously updated on the robot to adapt to the deployment environments. Our system therefore tightly couples multi-sensor perception and localisation to continuously learn from self-supervised ...Conference Paper