18 research outputs found
Parallel Reinforcement Learning Simulation for Visual Quadrotor Navigation
Reinforcement learning (RL) is an agent-based approach for teaching robots to
navigate within the physical world. Gathering data for RL is known to be a
laborious task, and real-world experiments can be risky. Simulators facilitate
the collection of training data in a quicker and more cost-effective manner.
However, RL frequently requires a significant number of simulation steps for an
agent to become skilful at simple tasks. This is a prevalent issue within the
field of RL-based visual quadrotor navigation where state dimensions are
typically very large and dynamic models are complex. Furthermore, rendering
images and obtaining physical properties of the agent can be computationally
expensive. To solve this, we present a simulation framework, built on AirSim,
which provides efficient parallel training. Building on this framework, Ape-X
is modified to incorporate decentralised training of AirSim environments to
make use of numerous networked computers. Through experiments we were able to
achieve a reduction in training time from 3.9 hours to 11 minutes using the
aforementioned framework and a total of 74 agents and two networked computers.
Further details including a github repo and videos about our project,
PRL4AirSim, can be found at https://sites.google.com/view/prl4airsim/homeComment: This work has been submitted to the IEEE International Conference on
Robotics and Automation (ICRA) for possible publication. Copyright may be
transferred without notice, after which this version may no longer be
accessibl
Sensor-driven online coverage planning for autonomous underwater vehicles
Abstract-At present, autonomous underwater vehicle (AUV) mine countermeasure (MCM) surveys are normally pre-planned by operators using ladder or zig-zag paths. Such surveys are conducted with side-looking sonar sensors whose performance is dependant on environmental, target, sensor, and AUV platform parameters. It is difficult to obtain precise knowledge of all of these parameters to be able to design optimal mission plans offline. This research represents the first known sensor driven online approach to seabed coverage for MCM. A method is presented where paths are planned using a multi-objective optimization. Information theory is combined with a new concept coined branch entropy based on a hexagonal cell decomposition. The result is a planning algorithm that not only produces shorter paths than conventional means, but is also capable of accounting for environmental factors detected in situ. Hardware-in-the-loop simulations and in water trials conducted on the IVER2 AUV show the effectiveness of the proposed method. Index Terms-autonomous underwater vehicles, coverage path planning, information gain, hardware-in-the-loop, mine countermeasure, sidescan sonar, adaptive mission plannin
Characterizing Visual Localization and Mapping Datasets
Benchmarking mapping and motion estimation algorithms is established practice in robotics and computer vision. As the diversity of datasets increases, in terms of the trajectories, models, and scenes, it becomes a challenge to select datasets for a given benchmarking purpose. Inspired by the Wasserstein distance, this paper addresses this concern by developing novel metrics to evaluate trajectories and the environments without relying on any SLAM or motion estimation algorithm. The metrics, which so far have been missing in the research community, can be applied to the plethora of datasets that exist. Additionally, to improve the robotics SLAM benchmarking, the paper presents a new dataset for visual localization and mapping algorithms. A broad range of real-world trajectories is used in very high-quality scenes and a rendering framework to create a set of synthetic datasets with ground-truth trajectory and dense map which are representative of key SLAM applications such as virtual reality (VR), micro aerial vehicle (MAV) flight, and ground robotics
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper
In this paper we investigate an emerging application, 3D scene understanding,
likely to be significant in the mobile space in the near future. The goal of
this exploration is to reduce execution time while meeting our quality of
result objectives. In previous work we showed for the first time that it is
possible to map this application to power constrained embedded systems,
highlighting that decision choices made at the algorithmic design-level have
the most impact.
As the algorithmic design space is too large to be exhaustively evaluated, we
use a previously introduced multi-objective Random Forest Active Learning
prediction framework dubbed HyperMapper, to find good algorithmic designs. We
show that HyperMapper generalizes on a recent cutting edge 3D scene
understanding algorithm and on a modern GPU-based computer architecture.
HyperMapper is able to beat an expert human hand-tuning the algorithmic
parameters of the class of Computer Vision applications taken under
consideration in this paper automatically. In addition, we use crowd-sourcing
using a 3D scene understanding Android app to show that the Pareto front
obtained on an embedded system can be used to accelerate the same application
on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from
2 to over 12.Comment: 10 pages, Keywords: design space exploration, machine learning,
computer vision, SLAM, embedded systems, GPU, crowd-sourcin
Application-oriented Design Space Exploration for SLAM Algorithms
In visual SLAM, there are many software and hardware parameters, such as algorithmic thresholds and GPU frequency, that need to be tuned; however, this tuning should also take into account the structure and motion of the camera. In this paper, we determine the complexity of the structure and motion with a few parameters calculated using information theory. Depending on this complexity and the desired performance metrics, suitable parameters are explored and determined. Additionally, based on the proposed structure and motion parameters, several applications are presented, including a novel active SLAM approach which guides the camera in such a way that the SLAM algorithm achieves the desired performance metrics. Real-world and simulated experimental results demonstrate the effectiveness of the proposed design space and its applications
InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset
Datasets have gained an enormous amount of popularity in the computer vision
community, from training and evaluation of Deep Learning-based methods to
benchmarking Simultaneous Localization and Mapping (SLAM). Without a doubt,
synthetic imagery bears a vast potential due to scalability in terms of amounts
of data obtainable without tedious manual ground truth annotations or
measurements. Here, we present a dataset with the aim of providing a higher
degree of photo-realism, larger scale, more variability as well as serving a
wider range of purposes compared to existing datasets. Our dataset leverages
the availability of millions of professional interior designs and millions of
production-level furniture and object assets -- all coming with fine geometric
details and high-resolution texture. We render high-resolution and high
frame-rate video sequences following realistic trajectories while supporting
various camera types as well as providing inertial measurements. Together with
the release of the dataset, we will make executable program of our interactive
simulator software as well as our renderer available at
https://interiornetdataset.github.io. To showcase the usability and uniqueness
of our dataset, we show benchmarking results of both sparse and dense SLAM
algorithms.Comment: British Machine Vision Conference (BMVC) 201
SLAMBench 3.0:Systematic Automated Reproducible Evaluation of SLAM Systems for Robot Vision Challenges and Scene Understanding
As the SLAM research area matures and the number of SLAM systems available increases, the need for frameworks that can objectively evaluate them against prior work grows. This new version of SLAMBench moves beyond traditional visual SLAM, and provides new support for scene understanding and non-rigid environments (dynamic SLAM). More concretely for dynamic SLAM, SLAMBench 3.0 includes the first publicly available implementation of DynamicFusion, along with an evaluation infrastructure. In addition, we include two SLAM systems (one dense, one sparse) augmented with convolutional neural networks for scene understanding, together with datasets and appropriate metrics. Through a series of use-cases, we demonstrate the newly incorporated algorithms, visulation aids and metrics (6 new metrics, 4 new datasets and 5 new algorithms)
SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM
SLAM is becoming a key component of robotics and augmented reality (AR)
systems. While a large number of SLAM algorithms have been presented, there has
been little effort to unify the interface of such algorithms, or to perform a
holistic comparison of their capabilities. This is a problem since different
SLAM applications can have different functional and non-functional
requirements. For example, a mobile phonebased AR application has a tight
energy budget, while a UAV navigation system usually requires high accuracy.
SLAMBench2 is a benchmarking framework to evaluate existing and future SLAM
systems, both open and close source, over an extensible list of datasets, while
using a comparable and clearly specified list of performance metrics. A wide
variety of existing SLAM algorithms and datasets is supported, e.g.
ElasticFusion, InfiniTAM, ORB-SLAM2, OKVIS, and integrating new ones is
straightforward and clearly specified by the framework. SLAMBench2 is a
publicly-available software framework which represents a starting point for
quantitative, comparable and validatable experimental research to investigate
trade-offs across SLAM systems