Irobot Corporation

Daniele Tamino

Artem Gritsenko

Vazgen Karapetyan

Zacharias Psarakis

Benjamin Pheil

Andrea M Okerholm Huttlin

Jose Luis Franco Monsalve

Yao Chen

Renaud Moser

Arman Karimian

Guanlai Li

Robot localization or mapping can be provided without requiring the expense or complexity an 'at-a-distance' sensor, such as a camera, a LIDAR sensor, or the like. Adjacency-derived landmark features can be used and non-unique landmark features can be accommodated. Uncertainty in robot pose can be tracked and compared to an adaptive threshold, and non-dock and dock-based localization behavior can be controlled based on the uncertainty, the adaptive threshold, one or more other thresholds, and the accessibility of available differently oriented landmark features, such as perpendicularly oriented straight wall segments landmark features. Available features can be sorted according to a quality metric, and path planning and navigation techniques are also included for helping obtain successful wall-following and localization observations.

Robot localization and mapping accommodating non-unique landmarks

An autonomous robot is maneuvered around a feature in an environment along a first trajectory. Data characterizing the first trajectory is stored as a trajectory landmark. The autonomous cleaning robot is maneuvered along a second trajectory. Data characterizing the second trajectory is compared to the trajectory landmark. Based on comparing the data characterizing the second trajectory to the trajectory landmark, it is determined that the first trajectory matches the second trajectory. A transform that aligns the first trajectory with the second trajectory is determined. The transform is applied to an estimate of a position of the autonomous cleaning robot as a correction of the estimate.

Trajectory-based localization and mapping

Robot localization or mapping can be provided without requiring the expense or complexity an 'at-a-distance' sensor, such as a camera, a LIDAR sensor, or the like. Adjacency-derived landmark features can be used and non-unique landmark features can be accommodated. Uncertainty in robot pose can be tracked and compared to an adaptive threshold, and non-dock and docks based localization behavior can be controlled based on the uncertainty, the adaptive threshold, one or more other thresholds, and the accessibility of available differently oriented landmark features, such as perpendicularly oriented straight wall segments landmark features. Available features can be sorted according to a quality metric, and path planning and navigation techniques are also included for helping obtain successful wall-following and localization observations.

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Monrovia, CA, United States of America

Daniele Tamino