11 research outputs found
Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models
This letter describes an incremental multimodal surface mapping methodology,
which represents the environment as a continuous probabilistic model. This
model enables high-resolution reconstruction while simultaneously compressing
spatial and intensity point cloud data. The strategy employed in this work
utilizes Gaussian mixture models (GMMs) to represent the environment. While
prior GMM-based mapping works have developed methodologies to determine the
number of mixture components using information-theoretic techniques, these
approaches either operate on individual sensor observations, making them
unsuitable for incremental mapping, or are not real-time viable, especially for
applications where high-fidelity modeling is required. To bridge this gap, this
letter introduces a spatial hash map for rapid GMM submap extraction combined
with an approach to determine relevant and redundant data in a point cloud.
These contributions increase computational speed by an order of magnitude
compared to state-of-the-art incremental GMM-based mapping. In addition, the
proposed approach yields a superior tradeoff in map accuracy and size when
compared to state-of-the-art mapping methodologies (both GMM- and not
GMM-based). Evaluations are conducted using both simulated and real-world data.
The software is released open-source to benefit the robotics community.Comment: 7 pages, 7 figures, under review at IEEE Robotics and Automation
Letter
Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models
This letter presents a continuous probabilistic modeling methodology for
spatial point cloud data using finite Gaussian Mixture Models (GMMs) where the
number of components are adapted based on the scene complexity. Few
hierarchical and adaptive methods have been proposed to address the challenge
of balancing model fidelity with size. Instead, state-of-the-art mapping
approaches require tuning parameters for specific use cases, but do not
generalize across diverse environments. To address this gap, we utilize a
self-organizing principle from information-theoretic learning to automatically
adapt the complexity of the GMM model based on the relevant information in the
sensor data. The approach is evaluated against existing point cloud modeling
techniques on real-world data with varying degrees of scene complexity.Comment: 8 pages, 6 figures, to appear in IEEE Robotics and Automation Letter
Collaborative Human-Robot Exploration via Implicit Coordination
This paper develops a methodology for collaborative human-robot exploration
that leverages implicit coordination. Most autonomous single- and multi-robot
exploration systems require a remote operator to provide explicit guidance to
the robotic team. Few works consider how to embed the human partner alongside
robots to provide guidance in the field. A remaining challenge for
collaborative human-robot exploration is efficient communication of goals from
the human to the robot. In this paper we develop a methodology that implicitly
communicates a region of interest from a helmet-mounted depth camera on the
human's head to the robot and an information gain-based exploration objective
that biases motion planning within the viewpoint provided by the human. The
result is an aerial system that safely accesses regions of interest that may
not be immediately viewable or reachable by the human. The approach is
evaluated in simulation and with hardware experiments in a motion capture
arena. Videos of the simulation and hardware experiments are available at:
https://youtu.be/7jgkBpVFIoE.Comment: 7 pages, 10 figures, to appear in the 2022 IEEE International
Symposium on Safety, Security, and Rescue Robotics (SSRR
Hierarchical Collision Avoidance for Adaptive-Speed Multirotor Teleoperation
This paper improves safe motion primitives-based teleoperation of a
multirotor by developing a hierarchical collision avoidance method that
modulates maximum speed based on environment complexity and perceptual
constraints. Safe speed modulation is challenging in environments that exhibit
varying clutter. Existing methods fix maximum speed and map resolution, which
prevents vehicles from accessing tight spaces and places the cognitive load for
changing speed on the operator. We address these gaps by proposing a high-rate
(10 Hz) teleoperation approach that modulates the maximum vehicle speed through
hierarchical collision checking. The hierarchical collision checker
simultaneously adapts the local map's voxel size and maximum vehicle speed to
ensure motion planning safety. The proposed methodology is evaluated in
simulation and real-world experiments and compared to a non-adaptive motion
primitives-based teleoperation approach. The results demonstrate the advantages
of the proposed teleoperation approach both in time taken and the ability to
complete the task without requiring the user to specify a maximum vehicle
speed.Comment: 8 pages, 8 figures, to be published in the 2022 IEEE International
Symposium on Safety, Security, and Rescue Robotics (SSRR
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