Abstract: The invention is related to methods and apparatus that use a visual sensor and dead reckoning sensors to process Simultaneous Localization and Mapping (SLAM). These techniques can be used in robot navigation. Advantageously, such visual techniques can be used to autonomously generate and update a map. Unlike with laser rangefinders, the visual techniques are economically practical in a wide range of applications and can be used in relatively dynamic environments, such as environments in which people move. Certain embodiments contemplate improvements to the front-end processing in a SLAM-based system. Particularly, certain of these embodiments contemplate a novel landmark matching process. Certain of these embodiments also contemplate a novel landmark creation process. Certain embodiments contemplate improvements to the back-end processing in a SLAM-based system. Particularly, certain of these embodiments contemplate algorithms for modifying the SLAM graph in real-time to achieve a more efficient structure.

Abstract: A system and method for mapping parameter data acquired by a robot mapping system is disclosed. Parameter data characterizing the environment is collected while the robot localizes itself within the environment using landmarks. Parameter data is recorded in a plurality of local grids, i.e., sub-maps associated with the robot position and orientation when the data was collected. The robot is configured to generate new grids or reuse existing grids depending on the robot's current pose, the pose associated with other grids, and the uncertainty of these relative pose estimates. The pose estimates associated with the grids are updated over time as the robot refines its estimates of the locations of landmarks from which determines its pose in the environment. Occupancy maps or other global parameter maps may be generated by rendering local grids into a comprehensive map indicating the parameter data in a global reference frame extending the dimensions of the environment.

Abstract: Vector Field SLAM is a method for localizing a mobile robot in an unknown environment from continuous signals such as WiFi or active beacons. Disclosed is a technique for localizing a robot in relatively large and/or disparate areas. This is achieved by using and managing more signal sources for covering the larger area. One feature analyzes the complexity of Vector Field SLAM with respect to area size and number of signals and then describe an approximation that decouples the localization map in order to keep memory and run-time requirements low. A tracking method for re-localizing the robot in the areas already mapped is also disclosed. This allows to resume the robot after is has been paused or kidnapped, such as picked up and moved by a user. Embodiments of the invention can comprise commercial low-cost products including robots for the autonomous cleaning of floors.

Abstract: Apparatus and methods for carpet drift estimation are disclosed. In certain implementations, a robotic device includes an actuator system to move the body across a surface. A first set of sensors can sense an actuation characteristic of the actuator system. For example, the first set of sensors can include odometry sensors for sensing wheel rotations of the actuator system. A second set of sensors can sense a motion characteristic of the body. The first set of sensors may be a different type of sensor than the second set of sensors. A controller can estimate carpet drift based at least on the actuation characteristic sensed by the first set of sensors and the motion characteristic sensed by the second set of sensors.

Abstract: A system and method for mapping parameter data acquired by a robot mapping system is disclosed. Parameter data characterizing the environment is collected while the robot localizes itself within the environment using landmarks. Parameter data is recorded in a plurality of local grids, i.e., sub-maps associated with the robot position and orientation when the data was collected. The robot is configured to generate new grids or reuse existing grids depending on the robot's current pose, the pose associated with other grids, and the uncertainty of these relative pose estimates. The pose estimates associated with the grids are updated over time as the robot refines its estimates of the locations of landmarks from which determines its pose in the environment. Occupancy maps or other global parameter maps may be generated by rendering local grids into a comprehensive map indicating the parameter data in a global reference frame extending the dimensions of the environment.

Abstract: A system and method for mapping parameter data acquired by a robot mapping system is disclosed. Parameter data characterizing the environment is collected while the robot localizes itself within the environment using landmarks. Parameter data is recorded in a plurality of local grids, i.e., sub-maps associated with the robot position and orientation when the data was collected. The robot is configured to generate new grids or reuse existing grids depending on the robot's current pose, the pose associated with other grids, and the uncertainty of these relative pose estimates. The pose estimates associated with the grids are updated over time as the robot refines its estimates of the locations of landmarks from which determines its pose in the environment. Occupancy maps or other global parameter maps may be generated by rendering local grids into a comprehensive map indicating the parameter data in a global reference frame extending the dimensions of the environment.

Abstract: A proximity sensor includes first and second sensors disposed on a sensor body adjacent to one another. The first sensor is one of an emitter and a receiver. The second sensor is the other one of an emitter and a receiver. A third sensor is disposed adjacent the second sensor opposite the first sensor. The third sensor is an emitter if the first sensor is an emitter or a receiver if the first sensor is a receiver. Each sensor is positioned at an angle with respect to the other two sensors. Each sensor has a respective field of view. A first field of view intersects a second field of view defining a first volume that detects a floor surface within a first threshold distance. The second field of view intersects a third field of view defining a second volume that detects a floor surface within a second threshold distance.

Abstract: A proximity sensor includes first and second sensors disposed on a sensor body adjacent to one another. The first sensor is one of an emitter and a receiver. The second sensor is the other one of an emitter and a receiver. A third sensor is disposed adjacent the second sensor opposite the first sensor. The third sensor is an emitter if the first sensor is an emitter or a receiver if the first sensor is a receiver. Each sensor is positioned at an angle with respect to the other two sensors. Each sensor has a respective field of view. A first field of view intersects a second field of view defining a first volume that detects a floor surface within a first threshold distance. The second field of view intersects a third field of view defining a second volume that detects a floor surface within a second threshold distance.

Abstract: A system and method for mapping parameter data acquired by a robot mapping system is disclosed. Parameter data characterizing the environment is collected while the robot localizes itself within the environment using landmarks. Parameter data is recorded in a plurality of local grids, i.e., sub-maps associated with the robot position and orientation when the data was collected. The robot is configured to generate new grids or reuse existing grids depending on the robot's current pose, the pose associated with other grids, and the uncertainty of these relative pose estimates. The pose estimates associated with the grids are updated over time as the robot refines its estimates of the locations of landmarks from which determines its pose in the environment. Occupancy maps or other global parameter maps may be generated by rendering local grids into a comprehensive map indicating the parameter data in a global reference frame extending the dimensions of the environment.

Abstract: A mobile robot system is provided that includes a docking station having at least two pose-defining fiducial markers. The pose-defining fiducial markers have a predetermined spatial relationship with respect to one another and/or to a reference point on the docking station such that a docking path to the base station can be determined from one or more observations of the at least two pose-defining fiducial markers. A mobile robot in the system includes a pose sensor assembly. A controller is located on the chassis and is configured to analyze an output signal from the pose sensor assembly. The controller is configured to determine a docking station pose, to locate the docking station pose on a map of a surface traversed by the mobile robot and to path plan a docking trajectory.

Abstract: Vector Field SLAM is a method for localizing a mobile robot in an unknown environment from continuous signals such as WiFi or active beacons. Disclosed is a technique for localizing a robot in relatively large and/or disparate areas. This is achieved by using and managing more signal sources for covering the larger area. One feature analyzes the complexity of Vector Field SLAM with respect to area size and number of signals and then describe an approximation that decouples the localization map in order to keep memory and run-time requirements low. A tracking method for re-localizing the robot in the areas already mapped is also disclosed. This allows to resume the robot after is has been paused or kidnapped, such as picked up and moved by a user. Embodiments of the invention can comprise commercial low-cost products including robots for the autonomous cleaning of floors.

Abstract: A system and method for mapping parameter data acquired by a robot mapping system is disclosed. Parameter data characterizing the environment is collected while the robot localizes itself within the environment using landmarks. Parameter data is recorded in a plurality of local grids, i.e., sub-maps associated with the robot position and orientation when the data was collected. The robot is configured to generate new grids or reuse existing grids depending on the robot's current pose, the pose associated with other grids, and the uncertainty of these relative pose estimates. The pose estimates associated with the grids are updated over time as the robot refines its estimates of the locations of landmarks from which determines its pose in the environment. Occupancy maps or other global parameter maps may be generated by rendering local grids into a comprehensive map indicating the parameter data in a global reference frame extending the dimensions of the environment.

Abstract: Apparatus and methods for carpet drift estimation are disclosed. In certain implementations, a robotic device includes an actuator system to move the body across a surface. A first set of sensors can sense an actuation characteristic of the actuator system. For example, the first set of sensors can include odometry sensors for sensing wheel rotations of the actuator system. A second set of sensors can sense a motion characteristic of the body. The first set of sensors may be a different type of sensor than the second set of sensors. A controller can estimate carpet drift based at least on the actuation characteristic sensed by the first set of sensors and the motion characteristic sensed by the second set of sensors.

Abstract: Vector Field SLAM is a method for localizing a mobile robot in an unknown environment from continuous signals such as WiFi or active beacons. Disclosed is a technique for localizing a robot in relatively large and/or disparate areas. This is achieved by using and managing more signal sources for covering the larger area. One feature analyzes the complexity of Vector Field SLAM with respect to area size and number of signals and then describe an approximation that decouples the localization map in order to keep memory and run-time requirements low. A tracking method for re-localizing the robot in the areas already mapped is also disclosed. This allows to resume the robot after is has been paused or kidnapped, such as picked up and moved by a user. Embodiments of the invention can comprise commercial low-cost products including robots for the autonomous cleaning of floors.

Abstract: Vector Field SLAM is a method for localizing a mobile robot in an unknown environment from continuous signals such as WiFi or active beacons. Disclosed is a technique for localizing a robot in relatively large and/or disparate areas. This is achieved by using and managing more signal sources for covering the larger area. One feature analyzes the complexity of Vector Field SLAM with respect to area size and number of signals and then describe an approximation that decouples the localization map in order to keep memory and run-time requirements low. A tracking method for re-localizing the robot in the areas already mapped is also disclosed. This allows to resume the robot after is has been paused or kidnapped, such as picked up and moved by a user. Embodiments of the invention can comprise commercial low-cost products including robots for the autonomous cleaning of floors.

Abstract: The invention is related to methods and apparatus that use a visual sensor and dead reckoning sensors to process Simultaneous Localization and Mapping (SLAM). These techniques can be used in robot navigation. Advantageously, such visual techniques can be used to autonomously generate and update a map. Unlike with laser rangefinders, the visual techniques are economically practical in a wide range of applications and can be used in relatively dynamic environments, such as environments in which people move. Certain embodiments contemplate improvements to the front-end processing in a SLAM-based system. Particularly, certain of these embodiments contemplate a novel landmark matching process. Certain of these embodiments also contemplate a novel landmark creation process. Certain embodiments contemplate improvements to the back-end processing in a SLAM-based system. Particularly, certain of these embodiments contemplate algorithms for modifying the SLAM graph in real-time to achieve a more efficient structure.

Abstract: A system for processing handwriting that uses an ordinary camera as an image input device. The output of a single camera is used to produce a probability function that indicates the likelihood of whether the pen is touching the paper. The function uses clues including ink on the page and/or shadows. Another embodiment uses both pen up and pen down information to dynamically time warp-fit the information to fit it to a template.

Abstract: A system for processing handwriting that uses an ordinary camera as an image input device. The output of a single camera is used to produce a probability function that indicates the likelihood of whether the pen is touching the paper. The function uses clues including ink on the page and/or shadows. Another embodiment uses both pen up and pen down information to dynamically time warp-fit the information to fit it to a template.