520,300 research outputs found
Pred-NBV: Prediction-guided Next-Best-View for 3D Object Reconstruction
Prediction-based active perception has shown the potential to improve the
navigation efficiency and safety of the robot by anticipating the uncertainty
in the unknown environment. The existing works for 3D shape prediction make an
implicit assumption about the partial observations and therefore cannot be used
for real-world planning and do not consider the control effort for
next-best-view planning. We present Pred-NBV, a realistic object shape
reconstruction method consisting of PoinTr-C, an enhanced 3D prediction model
trained on the ShapeNet dataset, and an information and control effort-based
next-best-view method to address these issues. Pred-NBV shows an improvement of
25.46% in object coverage over the traditional methods in the AirSim simulator,
and performs better shape completion than PoinTr, the state-of-the-art shape
completion model, even on real data obtained from a Velodyne 3D LiDAR mounted
on DJI M600 Pro.Comment: 6 pages, 4 figures, 2 tables. Accepted to IROS 202
Belief Tree Search for Active Object Recognition
Active Object Recognition (AOR) has been approached as an unsupervised
learning problem, in which optimal trajectories for object inspection are not
known and are to be discovered by reducing label uncertainty measures or
training with reinforcement learning. Such approaches have no guarantees of the
quality of their solution. In this paper, we treat AOR as a Partially
Observable Markov Decision Process (POMDP) and find near-optimal policies on
training data using Belief Tree Search (BTS) on the corresponding belief Markov
Decision Process (MDP). AOR then reduces to the problem of knowledge transfer
from near-optimal policies on training set to the test set. We train a Long
Short Term Memory (LSTM) network to predict the best next action on the
training set rollouts. We sho that the proposed AOR method generalizes well to
novel views of familiar objects and also to novel objects. We compare this
supervised scheme against guided policy search, and find that the LSTM network
reaches higher recognition accuracy compared to the guided policy method. We
further look into optimizing the observation function to increase the total
collected reward of optimal policy. In AOR, the observation function is known
only approximately. We propose a gradient-based method update to this
approximate observation function to increase the total reward of any policy. We
show that by optimizing the observation function and retraining the supervised
LSTM network, the AOR performance on the test set improves significantly.Comment: IROS 201
Active SLAM for autonomous underwater exploration
Exploration of a complex underwater environment without an a priori map is beyond the state of the art for autonomous underwater vehicles (AUVs). Despite several efforts regarding simultaneous localization and mapping (SLAM) and view planning, there is no exploration framework, tailored to underwater vehicles, that faces exploration combining mapping, active localization, and view planning in a unified way. We propose an exploration framework, based on an active SLAM strategy, that combines three main elements: a view planner, an iterative closest point algorithm (ICP)-based pose-graph SLAM algorithm, and an action selection mechanism that makes use of the joint map and state entropy reduction. To demonstrate the benefits of the active SLAM strategy, several tests were conducted with the Girona 500 AUV, both in simulation and in the real world. The article shows how the proposed framework makes it possible to plan exploratory trajectories that keep the vehicle’s uncertainty bounded; thus, creating more consistent maps.Peer ReviewedPostprint (published version
Active Image-based Modeling with a Toy Drone
Image-based modeling techniques can now generate photo-realistic 3D models
from images. But it is up to users to provide high quality images with good
coverage and view overlap, which makes the data capturing process tedious and
time consuming. We seek to automate data capturing for image-based modeling.
The core of our system is an iterative linear method to solve the multi-view
stereo (MVS) problem quickly and plan the Next-Best-View (NBV) effectively. Our
fast MVS algorithm enables online model reconstruction and quality assessment
to determine the NBVs on the fly. We test our system with a toy unmanned aerial
vehicle (UAV) in simulated, indoor and outdoor experiments. Results show that
our system improves the efficiency of data acquisition and ensures the
completeness of the final model.Comment: To be published on International Conference on Robotics and
Automation 2018, Brisbane, Australia. Project Page:
https://huangrui815.github.io/active-image-based-modeling/ The author's
personal page: http://www.sfu.ca/~rha55
Predicting the Next Best View for 3D Mesh Refinement
3D reconstruction is a core task in many applications such as robot
navigation or sites inspections. Finding the best poses to capture part of the
scene is one of the most challenging topic that goes under the name of Next
Best View. Recently, many volumetric methods have been proposed; they choose
the Next Best View by reasoning over a 3D voxelized space and by finding which
pose minimizes the uncertainty decoded into the voxels. Such methods are
effective, but they do not scale well since the underlaying representation
requires a huge amount of memory. In this paper we propose a novel mesh-based
approach which focuses on the worst reconstructed region of the environment
mesh. We define a photo-consistent index to evaluate the 3D mesh accuracy, and
an energy function over the worst regions of the mesh which takes into account
the mutual parallax with respect to the previous cameras, the angle of
incidence of the viewing ray to the surface and the visibility of the region.
We test our approach over a well known dataset and achieve state-of-the-art
results.Comment: 13 pages, 5 figures, to be published in IAS-1
Lessons for higher education institutions from the establishment of the Schools Partnership Trust in Leeds (Garforth) and its future development
"This report identifies strengths and challenges for the Garforth School Partnership Trust (SPT) in Leeds of having Leeds Trinity University College as the Higher Education (HE) external partner and trustee related to governance, strategic planning, curriculum and learner support. It outlines the rationale behind the model of trust school designed by the Garforth SPT – the first Pathfinder for school trusts – and the development of the partnership relationships. The report evaluates the successes of the HE and SPT partnership to date in relation to the SPT’s stated aims and objectives and explores the ongoing developments into sponsorship of academies.
This report is based on a study carried out with the involvement of Leeds Trinity, SPT trustees, school leaders and other key stakeholders" - page 2
Sustainable development : fourth annual assessment of progress by the Scottish Government
SDC Scotland’s annual assessment is based on a review of government policy across a range of topics from economy and energy to education, health, waste and biodiversity. The conclusions and recommendations are also based on discussions with expert groups in each policy area, government civil servants and a stakeholder survey.Publisher PD
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