5,756 research outputs found
ActiveRMAP: Radiance Field for Active Mapping And Planning
A high-quality 3D reconstruction of a scene from a collection of 2D images
can be achieved through offline/online mapping methods. In this paper, we
explore active mapping from the perspective of implicit representations, which
have recently produced compelling results in a variety of applications. One of
the most popular implicit representations - Neural Radiance Field (NeRF), first
demonstrated photorealistic rendering results using multi-layer perceptrons,
with promising offline 3D reconstruction as a by-product of the radiance field.
More recently, researchers also applied this implicit representation for online
reconstruction and localization (i.e. implicit SLAM systems). However, the
study on using implicit representation for active vision tasks is still very
limited. In this paper, we are particularly interested in applying the neural
radiance field for active mapping and planning problems, which are closely
coupled tasks in an active system. We, for the first time, present an RGB-only
active vision framework using radiance field representation for active 3D
reconstruction and planning in an online manner. Specifically, we formulate
this joint task as an iterative dual-stage optimization problem, where we
alternatively optimize for the radiance field representation and path planning.
Experimental results suggest that the proposed method achieves competitive
results compared to other offline methods and outperforms active reconstruction
methods using NeRFs.Comment: Under revie
Definition of the 2005 flight deck environment
A detailed description of the functional requirements necessary to complete any normal commercial flight or to handle any plausible abnormal situation is provided. This analysis is enhanced with an examination of possible future developments and constraints in the areas of air traffic organization and flight deck technologies (including new devices and procedures) which may influence the design of 2005 flight decks. This study includes a discussion on the importance of a systematic approach to identifying and solving flight deck information management issues, and a description of how the present work can be utilized as part of this approach. While the intent of this study was to investigate issues surrounding information management in 2005-era supersonic commercial transports, this document may be applicable to any research endeavor related to future flight deck system design in either supersonic or subsonic airplane development
A multi-layered fast marching method for unmanned surface vehicle path planning in a time-variant maritime environment
Concerns regarding the influence of the marine environment, such as surface currents and winds, on autonomous marine vehicles have been raised in recent years. A number of researchers have been working on the development of intelligent path planning algorithms to minimise the negative effects of environmental influences, however most of this work focuses on the platform of autonomous underwater vehicles (AUVs) with very little work on unmanned surface vehicles (USVs). This paper presents a novel multi-layered fast marching (MFM) method developed to generate practical trajectories for USVs when operating in a dynamic environment. This method constructs a synthetic environment framework, which incorporates the information of planning space and surface currents. In terms of the planning space, there are repelling and attracting forces, which are evaluated using an attractive/repulsive vector field construction process. The influence of surface currents is weighted against the obstacles in the planning space using a 4-regime risk strategy. A trajectory is then calculated using the anisotropic fast marching method. The complete algorithm has been tested and validated using simulated surface currents, and the performance of generated trajectories have been evaluated in terms of different optimisation criteria, such as the distance and energy consumption
A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments
This paper reports on a data-driven, interaction-aware motion prediction
approach for pedestrians in environments cluttered with static obstacles. When
navigating in such workspaces shared with humans, robots need accurate motion
predictions of the surrounding pedestrians. Human navigation behavior is mostly
influenced by their surrounding pedestrians and by the static obstacles in
their vicinity. In this paper we introduce a new model based on Long-Short Term
Memory (LSTM) neural networks, which is able to learn human motion behavior
from demonstrated data. To the best of our knowledge, this is the first
approach using LSTMs, that incorporates both static obstacles and surrounding
pedestrians for trajectory forecasting. As part of the model, we introduce a
new way of encoding surrounding pedestrians based on a 1d-grid in polar angle
space. We evaluate the benefit of interaction-aware motion prediction and the
added value of incorporating static obstacles on both simulation and real-world
datasets by comparing with state-of-the-art approaches. The results show, that
our new approach outperforms the other approaches while being very
computationally efficient and that taking into account static obstacles for
motion predictions significantly improves the prediction accuracy, especially
in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International
Conference on Robotics and Automation (ICRA) 201
A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments
This paper reports on a data-driven, interaction-aware motion prediction
approach for pedestrians in environments cluttered with static obstacles. When
navigating in such workspaces shared with humans, robots need accurate motion
predictions of the surrounding pedestrians. Human navigation behavior is mostly
influenced by their surrounding pedestrians and by the static obstacles in
their vicinity. In this paper we introduce a new model based on Long-Short Term
Memory (LSTM) neural networks, which is able to learn human motion behavior
from demonstrated data. To the best of our knowledge, this is the first
approach using LSTMs, that incorporates both static obstacles and surrounding
pedestrians for trajectory forecasting. As part of the model, we introduce a
new way of encoding surrounding pedestrians based on a 1d-grid in polar angle
space. We evaluate the benefit of interaction-aware motion prediction and the
added value of incorporating static obstacles on both simulation and real-world
datasets by comparing with state-of-the-art approaches. The results show, that
our new approach outperforms the other approaches while being very
computationally efficient and that taking into account static obstacles for
motion predictions significantly improves the prediction accuracy, especially
in cluttered environments.Comment: 8 pages, accepted for publication at the IEEE International
Conference on Robotics and Automation (ICRA) 201
Pre-Deployment Testing of Low Speed, Urban Road Autonomous Driving in a Simulated Environment
Low speed autonomous shuttles emulating SAE Level L4 automated driving using
human driver assisted autonomy have been operating in geo-fenced areas in
several cities in the US and the rest of the world. These autonomous vehicles
(AV) are operated by small to mid-sized technology companies that do not have
the resources of automotive OEMs for carrying out exhaustive, comprehensive
testing of their AV technology solutions before public road deployment. Due to
the low speed of operation and hence not operating on roads containing
highways, the base vehicles of these AV shuttles are not required to go through
rigorous certification tests. The way the driver assisted AV technology is
tested and allowed for public road deployment is continuously evolving but is
not standardized and shows differences between the different states where these
vehicles operate. Currently, AVs and AV shuttles deployed on public roads are
using these deployments for testing and improving their technology. However,
this is not the right approach. Safe and extensive testing in a lab and
controlled test environment including Model-in-the-Loop (MiL),
Hardware-in-the-Loop (HiL) and Autonomous-Vehicle-in-the-Loop (AViL) testing
should be the prerequisite to such public road deployments. This paper presents
three dimensional virtual modeling of an AV shuttle deployment site and
simulation testing in this virtual environment. We have two deployment sites in
Columbus of these AV shuttles through the Department of Transportation funded
Smart City Challenge project named Smart Columbus. The Linden residential area
AV shuttle deployment site of Smart Columbus is used as the specific example
for illustrating the AV testing method proposed in this paper
Towards reactive navigation and attention skills for 3D intelligent characters
This paper presents a neural design which is able to provide
the necessary reactive navigation and attention skills for 3D embodied
agents (virtual humanoids or characters). Based on Grossberg’s
neural model of conditioning [6], as recently implemented by Chang
and Gaudiando [7], and according to the Adaptative Resonance Theory
(ART) and the neuroscientific concepts associated, the neural design
introduced has been divided in two main phases. Firstly, an environmentcategorization
phase, where an on-line pattern recognition and categorization
of the current agent sensory input data is carried out by a self
organizing neural network, which will finally provide the agent’s short
term memory layer(STM). Secondly, and based on the classical conditioning
paradigm, the model will associate the interesting STM states,
from the navigation or attention points of view, to finally simulate these
necessary skills for 3D characters or humanoids. Finally, we will show
some experimental navigational results, through the integration of the
model presented in 3D virtual environments.Partially supported by the GVA-project CTIDIB-2002-182 (Spain)
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