U. Schwesinger, P. Versari, A. Broggi and R. Siegwart

it – Information Technology, 2015

In this work an overview of the motion planning and dynamic perception framework within the V-Charge project is presented. This framework enables the V-Charge car to autonomously navigate in dynamic mixed-traffic scenarios. Other traffic participants are detected, classified and tracked from a combination of stereo and wide-angle monocular cameras. Predictions of their future movements are generated utilizing infrastructure information. Safe motion plans are acquired with a system-compliant sampling-based local motion planner. We show the navigation performance of this vision-only autonomous vehicle in both simulation and real-world experiments.

@article{SchwesingerVBS15,
author = {Ulrich Schwesinger and
Pietro Versari and
Alberto Broggi and
Roland Siegwart},
title = {Vision-only fully automated driving in dynamic mixed-traffic scenarios},
journal = {it - Information Technology},
volume = {57},
number = {4},
pages = {231--242},
year = {2015},
url = {https://www.degruyter.com/view/j/itit.2015.57.issue-4/itit-2015-0005/itit-2015-0005.xml},
}


Barfoot, Furgale

Robotics, IEEE Transactions on, 30(3), pp. 679-693

In this paper, we provide specific and practical approaches to associate uncertainty with 4 ?? 4 transformation matrices, which is a common representation for pose variables in 3-D space. We show constraint-sensitive means of perturbing transformation matrices using their associated exponential-map generators and demonstrate these tools on three simple-yet-important estimation problems: 1) propagating uncertainty through a compound pose change, 2) fusing multiple measurements of a pose(e.g., for use in pose-graph relaxation), and 3) propagating uncertainty on poses (and landmarks) through a nonlinear camera model. The contribution of the paper is the presentation of the theoretical tools, which can be applied in the analysis of many problems involving 3-D pose and point variables.


@article{barfoot_tro14,
year = { 2014 },
title = { Associating Uncertainty with Three-Dimensional Poses for
use in Estimation Problems },
codeurl = { https://asrl.utias.utoronto.ca/code/barfoot_tro14.zip },
journal = { Robotics, IEEE Transactions on },
author = { Barfoot and Furgale },
pages = { 679-693 },
number = { 3 },
volume = { 30 },
month = { jun },
doi = { 10.1109/TRO.2014.2298059 },
}


Martin Rufli, Javier Alonso-Mora, Roland Siegwart

IEEE Transactions on Robotics (T-RO)

This paper addresses decentralized motion planning among a homogeneous set of feedback-controlled, decision-making agents. It introduces the continuous control obstacle (C^n-CO), which describes the set of C^n-continuous control sequences (and thus trajectories) that lead to a collision between interacting agents. By selecting a feasible trajectory from C^n-CO’s complement, a collision-free motion is obtained. The approach represents an extension to the reciprocal velocity obstacle (RVO, ORCA) collision-avoidance methods so that trajectory segments verify C^n continuity rather than piecewise linearity. This allows the large class of robots capable of tracking C^n-continuous trajectories to employ it for partial motion planning directly—rather than as a mere tool for collision checking. This paper further establishes that both the original velocity obstacle method and several of its recently developed reciprocal extensions (which treat specific robot physiologies only) correspond to particular instances of C^n-CO. In addition to the described extension in trajectory continuity, C^n-CO thus represents a unification of existing RVO theory. Finally, the presented method is validated in simulation—and a parameter study reveals under which environmental and control conditions C^n-CO with n>0 admits significantly improved navigation performance compared with inflated approaches based on ORCA.


@article{rufli_TRO13
author = {M Rufli and J Alonso-Mora and R Siegwart},
title = {Reciprocal Collision Avoidance With Motion Continuity Constraints},
journal = {IEEE Transactions on Robotics},
volume = {29},
number = {4},
year = {2013},
pages = {1-14}
}