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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 -
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 -
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