Neato Robotics, Inc.

Vladimir Ermakov

Boris Sofman

Steven Mathew Alexander

Mark Emmerich

Nathaniel David Monson

Pankaj Shah

Joe Augenbraun

Nick Donaldson

Mark Woodward

Linda Hirschhorn

William Jayne

Joseph Pinzaronne

Jb Gomez

Steve Alexander

Techniques that optimize performance of simultaneous localization and mapping (SLAM) processes for mobile devices, typically a mobile robot. In one embodiment, erroneous particles are introduced to the particle filtering process of localization. Monitoring the weights of the erroneous particles relative to the particles maintained for SLAM provides a verification that the robot is localized and detection that it is no longer localized. In another embodiment, cell-based grid mapping of a mobile robot's environment also monitors cells for changes in their probability of occupancy. Cells with a changing occupancy probability are marked as dynamic and updating of such cells to the map is suspended or modified until their individual occupancy probabilities have stabilized. In another embodiment, mapping is suspended when it is determined that the device is acquiring data regarding its physical environment in such a way that use of the data for mapping will incorporate distortions into the map, as for example when the robotic device is tilted.

Method and apparatus for simultaneous localization and mapping of mobile robot environment

A multi-function robotic device may have utility in various applications. In accordance with one aspect, a multi-function robotic device may be selectively configurable to perform a desired function in accordance with the capabilities of a selectively removable functional cartridge operably coupled with a robot body. Localization and mapping techniques may employ partial maps associated with portions of an operating environment, data compression, or both.

Localization and mapping system and method for a robotic device

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Santa Clara, CA, United States of America

Vladimir Ermakov