39 research outputs found
Aerial-Ground collaborative sensing: Third-Person view for teleoperation
Rapid deployment and operation are key requirements in time critical
application, such as Search and Rescue (SaR). Efficiently teleoperated ground
robots can support first-responders in such situations. However, first-person
view teleoperation is sub-optimal in difficult terrains, while a third-person
perspective can drastically increase teleoperation performance. Here, we
propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide
third-person perspective to ground robots. While our approach is based on local
visual servoing, it further leverages the global localization of several ground
robots to seamlessly transfer between these ground robots in GPS-denied
environments. Therewith one MAV can support multiple ground robots on a demand
basis. Furthermore, our system enables different visual detection regimes, and
enhanced operability, and return-home functionality. We evaluate our system in
real-world SaR scenarios.Comment: Accepted for publication in 2018 IEEE International Symposium on
Safety, Security and Rescue Robotics (SSRR
3D Registration of Aerial and Ground Robots for Disaster Response: An Evaluation of Features, Descriptors, and Transformation Estimation
Global registration of heterogeneous ground and aerial mapping data is a
challenging task. This is especially difficult in disaster response scenarios
when we have no prior information on the environment and cannot assume the
regular order of man-made environments or meaningful semantic cues. In this
work we extensively evaluate different approaches to globally register UGV
generated 3D point-cloud data from LiDAR sensors with UAV generated point-cloud
maps from vision sensors. The approaches are realizations of different
selections for: a) local features: key-points or segments; b) descriptors:
FPFH, SHOT, or ESF; and c) transformation estimations: RANSAC or FGR.
Additionally, we compare the results against standard approaches like applying
ICP after a good prior transformation has been given. The evaluation criteria
include the distance which a UGV needs to travel to successfully localize, the
registration error, and the computational cost. In this context, we report our
findings on effectively performing the task on two new Search and Rescue
datasets. Our results have the potential to help the community take informed
decisions when registering point-cloud maps from ground robots to those from
aerial robots.Comment: Awarded Best Paper at the 15th IEEE International Symposium on
Safety, Security, and Rescue Robotics 2017 (SSRR 2017
SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding
In order to operate in human environments, a robot's semantic perception has
to overcome open-world challenges such as novel objects and domain gaps.
Autonomous deployment to such environments therefore requires robots to update
their knowledge and learn without supervision. We investigate how a robot can
autonomously discover novel semantic classes and improve accuracy on known
classes when exploring an unknown environment. To this end, we develop a
general framework for mapping and clustering that we then use to generate a
self-supervised learning signal to update a semantic segmentation model. In
particular, we show how clustering parameters can be optimized during
deployment and that fusion of multiple observation modalities improves novel
object discovery compared to prior work. Models, data, and implementations can
be found at https://github.com/hermannsblum/scimComment: accepted at ISRR 202
U-BEV: Height-aware Bird's-Eye-View Segmentation and Neural Map-based Relocalization
Efficient relocalization is essential for intelligent vehicles when GPS
reception is insufficient or sensor-based localization fails. Recent advances
in Bird's-Eye-View (BEV) segmentation allow for accurate estimation of local
scene appearance and in turn, can benefit the relocalization of the vehicle.
However, one downside of BEV methods is the heavy computation required to
leverage the geometric constraints. This paper presents U-BEV, a U-Net inspired
architecture that extends the current state-of-the-art by allowing the BEV to
reason about the scene on multiple height layers before flattening the BEV
features. We show that this extension boosts the performance of the U-BEV by up
to 4.11 IoU. Additionally, we combine the encoded neural BEV with a
differentiable template matcher to perform relocalization on neural SD-map
data. The model is fully end-to-end trainable and outperforms transformer-based
BEV methods of similar computational complexity by 1.7 to 2.8 mIoU and
BEV-based relocalization by over 26% Recall Accuracy on the nuScenes dataset.Comment: This work has been submitted to the IEEE for possible publication.
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3D Localization, Mapping and Path Planning for Search and Rescue Operations
This work presents our results on 3D robot localization, mapping and path planning for the latest joint exercise of the European project 'Long-Term Human-Robot Teaming for Robots Assisted Disaster Response (TRADR). The full system is operated and evaluated by firemen end-users in real-world search and rescue experiments. We demonstrate that the system is able to plan a path to a goal position desired by the fireman operator in the TRADR Operational Control Unit (OCU), using a persistent 3D map created by the robot during previous sorties