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

Abstract

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.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]The present application claims the benefit of co-pending U.S. provisional application Ser. No. 61 / 238,597, filed Aug. 31, 2009, entitled “Computation Optimization Techniques for Simultaneous Localization and Mapping,” the disclosure of which is incorporated by reference herein in its entirety.BACKGROUND[0002]Aspects of the present invention relate to mobile robots, and more particularly to the mapping of environments in which mobile robots operate, to facilitate movement of mobile robots within those environments.[0003]As a system that enables a mobile robot to map its environment and maintain working data of its position within that map, simultaneous localization and mapping (SLAM) is both accurate and versatile. Its reliability and suitability for a variety of applications make it a useful element for imparting a robot with some level of autonomy.[0004]Typically, however, SLAM techniques tend to be computationally intensive and thus thei...

AI Technical Summary

Benefits of technology

[0006]Four concepts are outlined herein, each intended to enable a SLAM system to maintain efficiency when it is operating on a platform that provides limited processor

Problems solved by technology

Typically, however, SLAM techniques tend to be computationally intensive and thus their efficient execution often requires a level of processing power and memory capacity that may not be cost effective for some consumer product applications.

For those facing the low-cost production targets necessary for competition in the consumer market, it is unlikely that an economic hardware environment would include processing and memory capacities capable of supporting adequately a robust SLAM system.

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