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In this paper, we present a filtering method for estimating the shape and end effector pose of a highly articulated surgical snake robot. Our algorithm introduces new kinematic models that are used in the prediction step of an extended Kalman filter whose update step incorporates measurements from a 5-DOF electromagnetic tracking sensor situated at the distal end of the robot. A single tracking sensor is sufficient for estimating the shape of the system because the robot is inherently a follow-the-leader mechanism with well defined motion characteristics. We therefore show that, with appropriate steering motion, the state of the filter is fully observable. The goal of our shape estimation algorithm is to create a more accurate and representative 3D rendered visualization for image-guided surgery. We demonstrate the feasibility of our method with results from an animal experiment in which our shape and pose estimate was used as feedback in a control scheme that semi-autonomously drove the robot along the epicardial surface of a porcine heart.

We present a method for topological SLAM that specifically targets loop closing for edge-ordered graphs. Instead of using a heuristic approach to accept or reject loop closing, we propose a probabilistically grounded multi-hypothesis technique that relies on the incremental construction of a map/state hypothesis tree. Loop closing is introduced automatically within the tree expansion, and likely hypotheses are chosen based on their posterior probability after a sequence of sensor measurements. Careful pruning of the hypothesis tree keeps the growing number of hypotheses under control and a recursive formulation reduces storage and computational costs. Experiments are used to validate the approach.2 (1,3) a) b)2 (1,3) 1 (4,2,3) 1 (4,3,2) 3 (2,4,1) 4 (3,1) 4 (3,1) 3 (2,4,1)

We present a "leap-frog" path designed for a team of three robots performing cooperative localization. Two robots act as stationary measurement beacons while the third moves in a path that provides informative measurements. After completing the move, the roles of each robot are switched and the path is repeated. We demonstrate accurate localization using this path via a coverage experiment in which three robots successfully cover a 20m x 30m area. We report an approximate positional drift of 1.1m per robot over a travel distance of 140m. To our knowledge, this is one of the largest successful GPS-denied coverage experiments to date.

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