2,973 research outputs found
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 Exploration for Robust Object Detection
Today, mobile robots are increasingly expected to operate in ever more complex and dynamic environments. In order to carry out many of the higher-level tasks envisioned a semantic understanding of a workspace is pivotal. Here our field has benefited significantly from successes in machine learning and vision: applications in robotics of off-the-shelf object detectors are plentiful. This paper outlines an online, any-time planning framework enabling the active exploration of such detections. Our approach exploits the ability to move to different vantage points and implicitly weighs the benefits of gaining more certainty about the existence of an object against the physical cost of the exploration required. The result is a robot which plans trajectories specifically to decrease the entropy of putative detections. Our system is demonstrated to significantly improve detection performance and trajectory length in simulated and real robot experiments.National Science Foundation (U.S.) (IIS grant 0546467)United States. Office of Naval Research (MURI N1141207-236214
In-Network View Synthesis for Interactive Multiview Video Systems
To enable Interactive multiview video systems with a minimum view-switching
delay, multiple camera views are sent to the users, which are used as reference
images to synthesize additional virtual views via depth-image-based rendering.
In practice, bandwidth constraints may however restrict the number of reference
views sent to clients per time unit, which may in turn limit the quality of the
synthesized viewpoints. We argue that the reference view selection should
ideally be performed close to the users, and we study the problem of in-network
reference view synthesis such that the navigation quality is maximized at the
clients. We consider a distributed cloud network architecture where data stored
in a main cloud is delivered to end users with the help of cloudlets, i.e.,
resource-rich proxies close to the users. In order to satisfy last-hop
bandwidth constraints from the cloudlet to the users, a cloudlet re-samples
viewpoints of the 3D scene into a discrete set of views (combination of
received camera views and virtual views synthesized) to be used as reference
for the synthesis of additional virtual views at the client. This in-network
synthesis leads to better viewpoint sampling given a bandwidth constraint
compared to simple selection of camera views, but it may however carry a
distortion penalty in the cloudlet-synthesized reference views. We therefore
cast a new reference view selection problem where the best subset of views is
defined as the one minimizing the distortion over a view navigation window
defined by the user under some transmission bandwidth constraints. We show that
the view selection problem is NP-hard, and propose an effective polynomial time
algorithm using dynamic programming to solve the optimization problem.
Simulation results finally confirm the performance gain offered by virtual view
synthesis in the network
Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition
This paper presents a cognition-inspired agnostic framework for building a map for Visual Place Recognition. This framework draws inspiration from human-memorability, utilizes the traditional image entropy concept and computes the static content in an image; thereby presenting a tri-folded criteria to assess the `memorability' of an image for visual place recognition. A dataset namely `ESSEX3IN1' is created, composed of highly confusing images from indoor, outdoor and natural scenes for analysis. When used in conjunction with state-of-the-art visual place recognition methods, the proposed framework provides significant performance boost to these techniques, as evidenced by results on ESSEX3IN1 and other public datasets
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