952 research outputs found
Multitask Learning for Scalable and Dense Multilayer Bayesian Map Inference
This article presents a novel and flexible multitask multilayer Bayesian
mapping framework with readily extendable attribute layers. The proposed
framework goes beyond modern metric-semantic maps to provide even richer
environmental information for robots in a single mapping formalism while
exploiting intralayer and interlayer correlations. It removes the need for a
robot to access and process information from many separate maps when performing
a complex task, advancing the way robots interact with their environments. To
this end, we design a multitask deep neural network with attention mechanisms
as our front-end to provide heterogeneous observations for multiple map layers
simultaneously. Our back-end runs a scalable closed-form Bayesian inference
with only logarithmic time complexity. We apply the framework to build a dense
robotic map including metric-semantic occupancy and traversability layers.
Traversability ground truth labels are automatically generated from
exteroceptive sensory data in a self-supervised manner. We present extensive
experimental results on publicly available datasets and data collected by a 3D
bipedal robot platform and show reliable mapping performance in different
environments. Finally, we also discuss how the current framework can be
extended to incorporate more information such as friction, signal strength,
temperature, and physical quantity concentration using Gaussian map layers. The
software for reproducing the presented results or running on customized data is
made publicly available
Neural Implicit Surface Reconstruction using Imaging Sonar
We present a technique for dense 3D reconstruction of objects using an
imaging sonar, also known as forward-looking sonar (FLS). Compared to previous
methods that model the scene geometry as point clouds or volumetric grids, we
represent the geometry as a neural implicit function. Additionally, given such
a representation, we use a differentiable volumetric renderer that models the
propagation of acoustic waves to synthesize imaging sonar measurements. We
perform experiments on real and synthetic datasets and show that our algorithm
reconstructs high-fidelity surface geometry from multi-view FLS images at much
higher quality than was possible with previous techniques and without suffering
from their associated memory overhead.Comment: 8 pages, 8 figures. This paper is under revie
Open Source Robot Localization for Non-Planar Environments
The operational environments in which a mobile robot executes its missions
often exhibit non-flat terrain characteristics, encompassing outdoor and indoor
settings featuring ramps and slopes. In such scenarios, the conventional
methodologies employed for localization encounter novel challenges and
limitations. This study delineates a localization framework incorporating
ground elevation and inclination considerations, deviating from traditional 2D
localization paradigms that may falter in such contexts. In our proposed
approach, the map encompasses elevation and spatial occupancy information,
employing Gridmaps and Octomaps. At the same time, the perception model is
designed to accommodate the robot's inclined orientation and the potential
presence of ground as an obstacle, besides usual structural and dynamic
obstacles. We have developed and rigorously validated our approach within Nav2,
and esteemed open-source framework renowned for robot navigation. Our findings
demonstrate that our methodology represents a viable and effective alternative
for mobile robots operating in challenging outdoor environments or intrincate
terrains
Deep probabilistic methods for improved radar sensor modelling and pose estimation
Radar’s ability to sense under adverse conditions and at far-range makes it a valuable alternative to vision and lidar for mobile robotic applications. However, its complex, scene-dependent sensing process and significant noise artefacts makes working with radar challenging. Moving past classical rule-based approaches, which have dominated the literature to date, this thesis investigates deep and data-driven solutions across a range of tasks in robotics.
Firstly, a deep approach is developed for mapping raw sensor measurements to a grid-map of occupancy probabilities, outperforming classical filtering approaches by a significant margin. A distribution over the occupancy state is captured, additionally allowing uncertainty in predictions to be identified and managed. The approach is trained entirely using partial labels generated automatically from lidar, without requiring manual labelling.
Next, a deep model is proposed for generating stochastic radar measurements from simulated elevation maps. The model is trained by learning the forward and backward processes side-by-side, using a combination of adversarial and cyclical consistency constraints in combination with a partial alignment loss, using labels generated in lidar. By faithfully replicating the radar sensing process, new models can be trained for down-stream tasks, using labels that are readily available in simulation. In this case, segmentation models trained on simulated radar measurements, when deployed in the real world, are shown to approach the performance of a model trained entirely on real-world measurements.
Finally, the potential of deep approaches applied to the radar odometry task are explored. A learnt feature space is combined with a classical correlative scan matching procedure and optimised for pose prediction, allowing the proposed method to outperform the previous state-of-the-art by a significant margin. Through a probabilistic consideration the uncertainty in the pose is also successfully characterised. Building upon this success, properties of the Fourier Transform are then utilised to separate the search for translation and angle. It is shown that this decoupled search results in a significant boost to run-time performance, allowing the approach to run in real-time on CPUs and embedded devices, whilst remaining competitive with other radar odometry methods proposed in the literature
Adaptive Learning Terrain Estimation for Unmanned Aerial Vehicle Applications
For the past decade, terrain mapping research has focused on ground robots using occupancy grids and tree-like data structures, like Octomap and Quadtrees. Since flight vehicles have different constraints, ground-based terrain mapping research may not be directly applicable to the aerospace industry. To address this issue, Adaptive Learning Terrain Estimation algorithms have been developed with an aim towards aerospace applications. This thesis develops and tests Adaptive Learning Terrain Estimation algorithms using a custom test benchmark on representative aerospace cases: autonomous UAV landing and UAV flight through 3D urban environments. The fundamental objective of this thesis is to investigate the use of Adaptive Learning Terrain Estimation algorithms for aerospace applications and compare their performance to commonly used mapping techniques such as Quadtree and Octomap. To test the algorithms, point clouds were collected and registered in simulation and real environments. Then, the Adaptive Learning, Quadtree, and Octomap algorithms were applied to the data sets, both in real-time and offline. Finally, metrics of map size, accuracy, and running time were developed and implemented to quantify and compare the performance of the algorithms. The results show that Quadtree yields the computationally lightest maps, but it is not suitable for real-time implementation due to its lack of recursiveness. Adaptive Learning maps are computationally efficient due to the use of multiresolution grids. Octomap yields the most detailed maps, but it produces a high computational load. The results of the research show that Adaptive Learning algorithms have significant potential for real-time implementation in aerospace applications. Their low memory load and variable-sized grids make them viable candidates for future research and development
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