This paper documents a large scale, long-term autonomy dataset for robotics research collected on the University of Michigan's North Campus. The dataset consists of omnidirectional imagery, 3D lidar, planar lidar, GPS, and proprioceptive sensors for odometry collected using a Segway robot. The dataset was collected to facilitate research focusing on long-term autonomous operation in changing environments. The dataset is composed of 27 sessions spaced approximately biweekly over the course of 15 months. The sessions repeatedly explore the campus, both indoors and outdoors, on varying trajectories, and at different times of the day across all four seasons. This allows the dataset to capture many challenging elements including: moving obstacles (e.g. pedestrians, bicyclists and cars), changing lighting, varying viewpoint, seasonal and weather changes (e.g. falling leaves and snow), and long-term structural changes caused by construction projects. To further facilitate research, we also provide ground-truth pose for all sessions in a single frame of reference.

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Abstract-This paper examined how humans alter reach-tograsp behavior to compensate for environmentally-induced object orientation uncertainty. We used a novel motion tracking framework to capture hand-object interactions, as well as a custom cylindrical object to detect contacts. Subjects were instructed to reach, grasp, and lift the object with or without vision. The orientation of the object was randomly changed on each trial. We hypothesized subjects would use a reach-to-grasp strategy that minimizes post-contact adjustments. However, our results indicate that (1) subjects are more likely to use the hand as a sensing apparatus prior to contact, and (2) the reach-to-grasp kinematics may be optimized for efficient sensing of object orientation. Our findings could provide potential solution to efficient tactile sensing for robotic hand in unstructured environment.