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US 12,699,394 B1Granted
Autonomous platform guidance systems with unknown environment mapping
AssigneeTrifo, Inc.
InventorsZhe Zhang, Qingyu Chen, Yen-Cheng Liu, Weikai Li
Life of this patent
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Abstract
The described positional awareness techniques employing sensory data gathering and analysis hardware with reference to specific example implementations implement improvements in the use of sensors, techniques and hardware design that can enable specific embodiments to find new area to cover by a robot performing an area coverage task of an unexplored area. The sensory data are gathered from an operational camera and one or more auxiliary sensors.
Claims18
A system for obtaining position features for guiding a mobile platform, the system including:
- a mobile platform;
- a sensory interface coupled to one or more sensors, including at least some visual sensors and at least some inertial sensors, wherein at least one sensor of the one or more sensors senses one or more of:
- a position, a motion, or an environment of the mobile platform;
- a database comprising feature point data obtained from sets of feature points identified in frames of images previously obtained by the at least some visual sensors;
- a processor coupled to the sensory interface, the mobile platform, the database, and a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer instructions, which, when executed, cause the processor to guide and control the mobile platform and further cause the processor to:
- determine, based upon a parameter of the one or more sensors, a statistical window of confidence, wherein the statistical window of confidence comprises a threshold number of sensor readings of the one or more sensors per a threshold length of time, wherein the statistical window of confidence provides a statistical degree of confidence in the sensor readings;
- obtain sensor readings above the statistical degree of confidence using the statistical window of confidence;
- maintain time dependent tracking state data using the sensor readings obtained according to the statistical window of confidence, wherein the time dependent tracking state data includes:
- (i) 3D coordinate pose data (x, y, and theta) of the mobile platform, and (ii) one or more frames of sensor readings including at least some frames from the at least some visual sensors, and wherein sensor readings can include sensory information received from any one of a plurality of sensors available to the mobile platform;
- track a set of inertial readings from the at least some inertial sensors;
- compute a variance for each dimension of a total of 3 dimensions of the set of inertial readings;
- determine whether the mobile platform is moving or at rest based upon the variance computed;
- and
- (a) when the mobile platform is moving, perform extended Kalman filtering on the time dependent tracking state data and determine pose information from the time dependent tracking state data;
- and
- (b) when the mobile platform is static, provide a computed pose
- wherein the processor computes the computed pose by performing at least one of:
- (i) propagating, using information obtained from a gyroscopic sensor, a zero velocity applied to location information of a pose and change orientation information of the mobile platform;
- or
- (ii) adding a noise factor to a previous pose.
17 more elements
A non-transitory computer readable medium having instructions stored thereon, which, when executed, cause a processor to obtain position features for guiding a mobile platform, including causing the processor to:
- determine, based upon a parameter of one or more sensors, a statistical window of confidence, wherein the statistical window of confidence comprises a threshold number of sensor readings of the one or more sensors per a threshold length of time, wherein the statistical window of confidence provides a statistical degree of confidence in the sensor readings;
- obtain sensor readings above the statistical degree of confidence using the statistical window of confidence;
- maintain time dependent tracking state data using the sensor readings obtained according to the statistical window of confidence, wherein the time dependent tracking state data includes:
- (i) 3D coordinate pose data (x, y, and theta) of the mobile platform, and (ii) one or more frames of sensor readings including at least some frames from one or more visual sensors, and wherein sensor readings can include sensory information received from any one of a plurality of sensors available to the mobile platform;
- track a set of inertial readings from at least some inertial sensors;
- compute a variance for each dimension of a total of 3 dimensions of the set of inertial readings;
- determine whether the mobile platform is moving or at rest based upon the variance computed;
- and
- (a) when the mobile platform is moving, perform extended Kalman filtering on the time dependent tracking state data and determine pose information from the time dependent tracking state data;
- and
- (b) when the mobile platform is static, provide a computed pose
- wherein a processor computes the computed pose by performing at least one of:
- (i) propagating, using information obtained from a gyroscopic sensor, a zero velocity applied to location information of a pose and change orientation information of the mobile platform;
- or
- (ii) adding a noise factor to a previous pose.
12 more elements
A method of obtaining position features for guiding a mobile platform, including:
- determine, based upon a parameter of one or more sensors, a statistical window of confidence, wherein the statistical window of confidence comprises a threshold number of sensor readings of the one or more sensors per a threshold length of time, wherein the statistical window of confidence provides a statistical degree of confidence in the sensor readings;
- obtain sensor readings above the statistical degree of confidence using the statistical window of confidence;
- maintain time dependent tracking state data using the sensor readings obtained according to the statistical window of confidence, wherein the time dependent tracking state data includes:
- (i) 3D coordinate pose data (x, y, and theta) of the mobile platform, and (ii) one or more frames of sensor readings including at least some frames from one or more visual sensors, and wherein sensor readings can include sensory information received from any one of a plurality of sensors available to the mobile platform;
- track a set of inertial readings from at least some inertial sensors;
- compute a variance for each dimension of a total of 3 dimensions of the set of inertial readings;
- determine whether the mobile platform is moving or at rest based upon the variance computed;
- and
- (a) when the mobile platform is moving, perform extended Kalman filtering on the time dependent tracking state data and determine pose information from the time dependent tracking state data;
- and
- (b) when the mobile platform is static, provide a computed pose
- wherein the computed pose is computed by performing at least one of:
- (i) propagating, using information obtained from a gyroscopic sensor, a zero velocity applied to location information of a pose and change orientation information of the mobile platform;
- or
- (ii) adding a noise factor to a previous pose.
12 more elements
Dependencies are inferred from the claim text.