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John Wang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

IROS Conference 2016 Conference Paper

AprilTag 2: Efficient and robust fiducial detection

John Wang
Edwin Olson

AprilTags and other passive fiducial markers require specialized algorithms to detect markers among other features in a natural scene. The vision processing steps generally dominate the computation time of a tag detection pipeline, so even small improvements in marker detection can translate to a faster tag detection system. We incorporated lessons learned from implementing and supporting the AprilTag system into this improved system. This work describes AprilTag 2, a completely redesigned tag detector that improves robustness and efficiency compared to the original AprilTag system. The tag coding scheme is unchanged, retaining the same robustness to false positives inherent to the coding system. The new detector improves performance with higher detection rates, fewer false positives, and lower computational time. Improved performance on small images allows the use of decimated input images, resulting in dramatic gains in detection speed.

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ICRA Conference 2014 Conference Paper

Robust pose graph optimization using stochastic gradient descent

John Wang
Edwin Olson

Robust SLAM methods can allow robots to recover correct maps even in the presence of incorrect loop closures. While these approaches improve robustness to outliers, they are susceptible to getting caught in local minima, a problem which is exacerbated by poor initial estimates. In this paper, we describe a stochastic gradient descent optimization approach that exhibits greater robustness to poor initial estimates. Our approach can either be used as a stand-alone optimization system or in conjunction with existing methods such as Gauss-Newton solvers. Using a combination of synthetic and real-world datasets, we demonstrate that our proposed approach is able to recover correct pose graphs significantly more frequently than other methods when large initialization errors are present.

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IROS Conference 2008 Conference Paper

Scalable Bayesian human-robot cooperation in mobile sensor networks

Frédéric Bourgault
Aakash Chokshi
John Wang
Danelle C. Shah
Jonathan R. Schoenberg
Ramnath Iyer
Franco Cedano
Mark E. Campbell

In this paper, scalable collaborative human-robot systems for information gathering applications are approached as a decentralized Bayesian sensor network problem. Human-computer augmented nodes and autonomous mobile sensor platforms are collaborating on a peer-to-peer basis by sharing information via wireless communication network. For each node, a computer (onboard the platform or carried by the human) implements both a decentralized Bayesian data fusion algorithm and a decentralized Bayesian control negotiation algorithm. The individual node controllers iteratively negotiate anonymously with each other in the information space to find cooperative search plans based on both observed and predicted information that explicitly consider the platforms (humans and robots) motion models, their sensors detection functions, as well as the target arbitrary motion model. The results of a collaborative multi-target search experiment conducted with a team of four autonomous mobile sensor platforms and five humans carrying small portable computers with wireless communication are presented to demonstrate the efficiency of the approach.

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