Fast vision-guided mobile robot navigation using model-based reasoning and prediction of uncertainties

Kosaka, Akio; Kak, Avinash C.

doi:10.1016/1049-9660(92)90045-5

Cited by 173 publications

(38 citation statements)

References 21 publications

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“…Mapless vision approaches -with no global environment representationcan be found to: be based in optical-flow [15] and template appearance matching [16], [17], track landmark features [18], [19] and more recently relevant, directly classify visual input via CNNs [20], [21]. In this work, we formulate a 2D global approximation of the environment (storing visited positions) inspired by occupancy grid maps [22], [23], [24], as opposed to post-exploration map-building approaches [25] and topological map representations [26].…”

Section: Further Related Work a Categorisation For Navigationmentioning

confidence: 99%

Deep Learning for Exploration and Recovery of Uncharted and Dynamic Targets from UAV-like Vision

Andrew

Greatwood

Burghardt

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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This paper discusses deep learning for solving static and dynamic search and recovery tasks-such as the retrieval of all instances of actively moving targets-based on partial-view Unmanned Aerial Vehicle (UAV)-like sensing. In particular, we demonstrate that abstracted tactic and strategic explorational agency can be implemented effectively via a single deep network that optimises in unity: the mapping of sensory inputs and positional history towards navigational actions. We propose a dual-stream classification paradigm that integrates one Convolutional Neural Network (CNN) for sensory processing with a second one for interpreting an evolving longterm map memory. In order to learn effective search behaviours given agent location and agent-centric sensory inputs, we train this design against 400k+ optimal navigational decision samples from each set of static and dynamic evolutions for different multi-target behaviour classes. We quantify recovery performance across an extensive range of scenarios; including probabilistic placement and dynamics, as well as fully random target walks and herd-inspired behaviours. Detailed results comparisons show that our design can outperform naïve, independent stream and off-the-shelf DRQN solutions. We conclude that the proposed dual-stream architecture can provide a unified, rationally motivated and effective architecture for solving online search tasks in dynamic, multi-target environments. With this paper we publish 3 key source code and associated models.

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Section: Further Related Work a Categorisation For Navigationmentioning

confidence: 99%

Deep Learning for Exploration and Recovery of Uncharted and Dynamic Targets from UAV-like Vision

Andrew

Greatwood

Burghardt

2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…This general approach is applied e.g. in [18] and [5]. Some specific details related to our implementation are given in the following.…”

Section: Hypothesis Generationmentioning

confidence: 99%

Global Localization Based on 3D Planar Surface Segments Detected by a 3D Camera

Cupec¹,

Nyarko²,

Filko³

et al. 2014

Proceedings of the Croatian Computer Vision Workshop

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Global localization of a mobile robot using planar surface segments extracted from depth images is considered. The robot's environment is represented by a topological map consisting of local models, each representing a particular location modeled by a set of planar surface segments. The discussed localization approach segments a depth image acquired by a 3D camera into planar surface segments which are then matched to model surface segments. The robot pose is estimated by the Extended Kalman Filter using surface segment pairs as measurements. The reliability and accuracy of the considered approach are experimentally evaluated using a mobile robot equipped by a Microsoft Kinect sensor.

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“…There is a class of localization algorithms that compare edges extracted from images with 3D-edge maps. One of the initial edge-based localization techniques was developed in Kosaka and Kak (1992) for navigating indoor hallways using a 3D-edge map of the doors and walls. This type of technique has, for the most part, been applied in indoor environments.…”

Section: Related Workmentioning

confidence: 99%

Robust outdoor visual localization using a three‐dimensional‐edge map

Nuske

Roberts

Wyeth

2009

Journal of Field Robotics

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Visual localization systems that are practical for autonomous vehicles in outdoor industrial applications must perform reliably in a wide range of conditions. Changing outdoor conditions cause difficulty by drastically altering the information available in the camera images. To confront the problem, we have developed a visual localization system that uses a surveyed three-dimensional (3D)-edge map of permanent structures in the environment. The map has the invariant properties necessary to achieve long-term robust operation. Previous 3D-edge map localization systems usually maintain a single pose hypothesis, * Currently Postdoctoral Fellow at the Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 (e-mail: nuske@cmu.edu). making it difficult to initialize without an accurate prior pose estimate and also making them susceptible to misalignment with unmapped edges detected in the camera image. A multihypothesis particle filter is employed here to perform the initialization procedure with significant uncertainty in the vehicle's initial pose. A novel observation function for the particle filter is developed and evaluated against two existing functions. The new function is shown to further improve the abilities of the particle filter to converge given a very coarse estimate of the vehicle's initial pose. An intelligent exposure control algorithm is also developed that improves the quality of the pertinent information in the image. Results gathered over an entire sunny day and also during rainy weather illustrate that the localization system can operate in a wide range of outdoor conditions. The conclusion is that an invariant map, a robust multihypothesis localization algorithm, and an intelligent exposure control algorithm all combine to enable reliable visual localization through challenging outdoor conditions. C 2009 Wiley Periodicals, Inc. Journal of Field

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Fast vision-guided mobile robot navigation using model-based reasoning and prediction of uncertainties

Cited by 173 publications

References 21 publications

Deep Learning for Exploration and Recovery of Uncharted and Dynamic Targets from UAV-like Vision

Deep Learning for Exploration and Recovery of Uncharted and Dynamic Targets from UAV-like Vision

Global Localization Based on 3D Planar Surface Segments Detected by a 3D Camera

Robust outdoor visual localization using a three‐dimensional‐edge map

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