Abstract-Reliability and availability are major concerns for autonomous systems. A personal robot has to solve complex tasks, such as loading a dishwasher or folding laundry, which are very difficult to automate robustly. In order for a robot to perform better in those applications, it needs to be capable of accepting help from a human operator.Shared autonomy is a system model based on human-robot dialogue. This work aims at bridging the gap between full human control and full autonomy for tasks in the domain of personal robotics. One of the hardest problems for personal robotic systems is perception: perceiving and inferring about objects in the robot's environment. We present a system capable of solving the perceptual inference in combination with a human, such that a human operator functions as a resource for the robot and helps to compensate for limitations of autonomy.In this paper, we show how a human-robot team can work together effectively to solve complex perception tasks. We present a system that asks a human operator to identify objects it doesn't recognize or find. In various experiments with the PR2 robot we show that this shared autonomy system performs more robustly than the robot system alone and that it is capable of tasks which are difficult to accomplish by an autonomous agent.

Bundle adjustment (BA) is the problem of refining a visual reconstruction to produce better structure and viewing parameter estimates. This problem is often formulated as a nonlinear least squares problem, where data arises from interest point matching. Mismatched interest points cause serious problems in this approach, as a single mismatch will affect the entire reconstruction. In this paper, we propose a novel robust Student's t BA algorithm (RST-BA). We model reprojection errors using the heavy tailed Student's t-distribution, and use an implicit trust region method to compute the maximum a posteriori (MAP) estimate of the camera and viewing parameters in this model. The resulting algorithm exploits the sparse structure essential for reconstructing multi-image scenarios, has the same time complexity as standard L 2 bundle adjustment (L 2 -BA), and can be implemented with minimal changes to the standard least squares framework. We show that the RST-BA is more accurate than either L 2 -BA or L 2 -BA with a σ-edit rule for outlier removal for a range of simulated error generation scenarios. The new method has also been used to reconstruct lunar topography using data from the NASA Apollo 15 orbiter, and we present visual and quantitative comparisons of RST-BA and L 2 -BA methods on this application. In particular, using the RST-BA algorithm we were able to reconstruct a DEM from unprocessed data with many outliers and no ground control points, which was not possible with the L 2 -BA method.

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