126 research outputs found
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
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
3D Multi-Robot Exploration with a Two-Level Coordination Strategy and Prioritization
This work presents a 3D multi-robot exploration framework for a team of UGVs
moving on uneven terrains. The framework was designed by casting the two-level
coordination strategy presented in [1] into the context of multi-robot
exploration. The resulting distributed exploration technique minimizes and
explicitly manages the occurrence of conflicts and interferences in the robot
team. Each robot selects where to scan next by using a receding horizon
next-best-view approach [2]. A sampling-based tree is directly expanded on
segmented traversable regions of the terrain 3D map to generate the candidate
next viewpoints. During the exploration, users can assign locations with higher
priorities on-demand to steer the robot exploration toward areas of interest.
The proposed framework can be also used to perform coverage tasks in the case a
map of the environment is a priori provided as input. An open-source
implementation is available online
NeU-NBV: Next Best View Planning Using Uncertainty Estimation in Image-Based Neural Rendering
Autonomous robotic tasks require actively perceiving the environment to
achieve application-specific goals. In this paper, we address the problem of
positioning an RGB camera to collect the most informative images to represent
an unknown scene, given a limited measurement budget. We propose a novel
mapless planning framework to iteratively plan the next best camera view based
on collected image measurements. A key aspect of our approach is a new
technique for uncertainty estimation in image-based neural rendering, which
guides measurement acquisition at the most uncertain view among view
candidates, thus maximising the information value during data collection. By
incrementally adding new measurements into our image collection, our approach
efficiently explores an unknown scene in a mapless manner. We show that our
uncertainty estimation is generalisable and valuable for view planning in
unknown scenes. Our planning experiments using synthetic and real-world data
verify that our uncertainty-guided approach finds informative images leading to
more accurate scene representations when compared against baselines.Comment: Accepted to IEEE/RSJ International Conference on Robotics and
Intelligent Systems (IROS) 202
MAP-NBV: Multi-agent Prediction-guided Next-Best-View Planning for Active 3D Object Reconstruction
We propose MAP-NBV, a prediction-guided active algorithm for 3D
reconstruction with multi-agent systems. Prediction-based approaches have shown
great improvement in active perception tasks by learning the cues about
structures in the environment from data. But these methods primarily focus on
single-agent systems. We design a next-best-view approach that utilizes
geometric measures over the predictions and jointly optimizes the information
gain and control effort for efficient collaborative 3D reconstruction of the
object. Our method achieves 22.75% improvement over the prediction-based
single-agent approach and 15.63% improvement over the non-predictive
multi-agent approach. We make our code publicly available through our project
website: http://raaslab.org/projects/MAPNBV/Comment: 7 pages, 7 figures, 2 tables. Submitted to MRS 202
Sampling-Based Exploration Strategies for Mobile Robot Autonomy
A novel, sampling-based exploration strategy is introduced for Unmanned Ground Vehicles (UGV) to efficiently map large GPS-deprived underground environments. It is compared to state-of-the-art approaches and performs on a similar level, while it is not designed for a specific robot or sensor configuration like the other approaches. The introduced exploration strategy, which is called Random-Sampling-Based Next-Best View Exploration (RNE), uses a Rapidly-exploring Random Graph (RRG) to find possible view points in an area around the robot. They are compared with a computation-efficient Sparse Ray Polling (SRP) in a voxel grid to find the next-best view for the exploration. Each node in the exploration graph built with RRG is evaluated regarding the ability of the UGV to traverse it, which is derived from an occupancy grid map. It is also used to create a topology-based graph where nodes are placed centrally to reduce the risk of collisions and increase the amount of observable space. Nodes that fall outside the local exploration area are stored in a global graph and are connected with a Traveling Salesman Problem solver to explore them later
The Surface Edge Explorer (SEE): A measurement-direct approach to next best view planning
High-quality observations of the real world are crucial for a variety of
applications, including producing 3D printed replicas of small-scale scenes and
conducting inspections of large-scale infrastructure. These 3D observations are
commonly obtained by combining multiple sensor measurements from different
views. Guiding the selection of suitable views is known as the NBV planning
problem.
Most NBV approaches reason about measurements using rigid data structures
(e.g., surface meshes or voxel grids). This simplifies next best view selection
but can be computationally expensive, reduces real-world fidelity, and couples
the selection of a next best view with the final data processing.
This paper presents the Surface Edge Explorer, a NBV approach that selects
new observations directly from previous sensor measurements without requiring
rigid data structures. SEE uses measurement density to propose next best views
that increase coverage of insufficiently observed surfaces while avoiding
potential occlusions. Statistical results from simulated experiments show that
SEE can attain similar or better surface coverage with less observation time
and travel distance than evaluated volumetric approaches on both small- and
large-scale scenes. Real-world experiments demonstrate SEE autonomously
observing a deer statue using a 3D sensor affixed to a robotic arm.Comment: Under review for the International Journal of Robotics Research
(IJRR), Manuscript #IJR-22-4541. 25 pages, 17 figures, 6 tables. Videos
available at https://www.youtube.com/watch?v=dqppqRlaGEA and
https://www.youtube.com/playlist?list=PLbaQBz4TuPcyNh4COoaCtC1ZGhpbEkFE
- …