Abstract: A map updating technique offers the performance advantages gained by robots using a good-quality static map for autonomous navigation within an environment, while providing the convenience of automatic updating. In particular, one or more robots log data while performing autonomous navigation in the environment according to the static map, and a computer appliance, such as a centralized server, collects the logged data as historic logged data, and performs a map update process using the historic logged data, Such operations provide for periodic or as needed updating of the static map, based on observational data from the robot(s) that capture changes in the environment, without need for taking the robot(s) offline for the computation.

Abstract: The present invention comprises an apparatus and method for monitoring a zone, which is a contiguous or non-contiguous, two-dimensional (2D) area or three-dimensional (3D) volume. Among its several advantages, the apparatus includes a plurality of sensors that monitor portions of the zone and report intrusion status to a control unit that provides monitoring boundary information to each of the sensors based on user input and further “maps” or otherwise associates each sensor to control unit outputs in accordance with user-defined behaviors. Still further, as a meaningful aid for configuring monitoring boundaries used by the sensors for objection intrusion detection, in one or more embodiments of the apparatus and method, at least a subset of sensors use a common coordinate frame of reference, based on a common origin located within overlapping sensor fields of view.

Abstract: According to one aspect of the teachings herein, a method and apparatus detect mechanical misalignments in a machine vision system during run-time operation of the machine vision system, and compensate image processing based on the detected misalignments, unless the detected misalignments are excessive. Excessive misalignments may be detected by determining a worst-case error based on them. If the worst-case error exceeds a defined limit, the machine vision system transitions to a fault state. The fault state may include disrupting operation of a hazardous machine or performing one or more other fault-state operations. Among the detected misalignments are internal misalignments within individual cameras used for imaging, and relative misalignments between cameras. The method and apparatus may further perform run-time verifications of focus and transition the machine vision system to a fault state responsive to detecting insufficient focal quality.

Abstract: The present invention comprises an apparatus and method for monitoring a zone, which is a contiguous or non-contiguous, two-dimensional (2D) area or three-dimensional (3D) volume. Among its several advantages, the apparatus includes a plurality of sensors that monitor portions of the zone and report intrusion status to a control unit that provides monitoring boundary information to each of the sensors based on user input and further “maps” or otherwise associates each sensor to control unit outputs in accordance with user-defined behaviors. Still further, as a meaningful aid for configuring monitoring boundaries used by the sensors for objection intrusion detection, in one or more embodiments of the apparatus and method, at least a subset of sensors use a common coordinate frame of reference, based on a common origin located within overlapping sensor fields of view.

Abstract: The teachings herein provide a method and apparatus for detecting erroneous pixel addressing in an imaging sensor. The method and apparatus advantageously leverage the characteristic “fixed pattern noise” of the sensor to detect addressing errors. Broadly, pixel addressing errors are detected based on comparing the pattern noise seen in data read outs from a targeted address of the sensor with characteristic fixed pattern noise known for the sensor.

Abstract: The teachings herein provide a method and apparatus for detecting erroneous pixel addressing in an imaging sensor. The method and apparatus advantageously leverage the characteristic “fixed pattern noise” of the sensor to detect addressing errors. Broadly, pixel addressing errors are detected based on comparing the pattern noise seen in data read outs from a targeted address of the sensor with characteristic fixed pattern noise known for the sensor.

Abstract: According to one aspect of the teachings presented herein, a machine vision system includes one or more sensor units that are each advantageously configured to use different pairings among a set of spaced-apart image sensors, to provide redundant object detection for a primary monitoring zone, while simultaneously providing for the detection of objects that may shadow the primary monitoring zone. Further, a plurality of “mitigations” and enhancements provide safety-of-design and robust operation. Such mitigations and enhancements include, for example, bad-pixel detection and mapping, cluster-based pixel processing for improved object detection, test-image injection for fault detection, dual-channel, redundant processing for safety-critical object detection, temporal filtering to reduce false detections, and the use of high dynamic range (HDR) images, for improved operation over variable ambient lighting conditions.

Abstract: According to one aspect of the teachings presented herein, a projective volume monitoring apparatus is configured to detect objects intruding into a monitoring zone. The projective volume monitoring apparatus is configured to detect the intrusion of objects of a minimum object size relative to a protection boundary, based on an advantageous processing technique that represents range pixels obtained from stereo correlation processing in spherical coordinates and maps those range pixels to a two-dimensional histogram that is defined over the projective coordinate space associated with capturing the stereo images used in correlation processing. The histogram quantizes the horizontal and vertical solid angle ranges of the projective coordinate space into a grid of cells. The apparatus flags range pixels that are within the protection boundary and accumulates them into corresponding cells of the histogram, and then performs clustering on the histogram cells to detect object intrusions.

Abstract: According to one aspect of the teachings presented herein, a projective volume monitoring apparatus is configured to protect against the intrusion of crawling and walking persons into a monitoring zone. The apparatus acquires stereo images of a first monitoring zone that begins at a defined height above a floor and of a second monitoring zone that lies below the first monitoring zone and extends downward to a floor or other surface on which persons may walk or crawl, and processes the stereo images to obtain range pixels. The apparatus detects object intrusions within the first monitoring zone by processing the corresponding range pixels using first object detection parameters that are tuned for the detection of walking or running persons, and detects object intrusions within the second monitoring zone by processing the corresponding range pixels using second object detection parameters that are tuned for the detection of crawling or prone persons.

Abstract: According to one aspect of the teachings herein, a method and apparatus detect mechanical misalignments in a machine vision system during run-time operation of the machine vision system, and compensate image processing based on the detected misalignments, unless the detected misalignments are excessive. Excessive misalignments may be detected by determining a worst-case error based on them. If the worst-case error exceeds a defined limit, the machine vision system transitions to a fault state. The fault state may include disrupting operation of a hazardous machine or performing one or more other fault-state operations. Among the detected misalignments are internal misalignments within individual cameras used for imaging, and relative misalignments between cameras. The method and apparatus may further perform run-time verifications of focus and transition the machine vision system to a fault state responsive to detecting insufficient focal quality.

Abstract: The teachings herein provide a method and apparatus for detecting erroneous pixel addressing in an imaging sensor. The method and apparatus advantageously leverage the characteristic “fixed pattern noise” of the sensor to detect addressing errors. Broadly, pixel addressing errors are detected based on comparing the pattern noise seen in data read outs from a targeted address of the sensor with characteristic fixed pattern noise known for the sensor.

Abstract: In one aspect of the teachings herein, a 3D imaging apparatus uses a “texture tool” or facilitates the use of such a tool by an operator, to add artificial texture to a scene being imaged, for 3D imaging of surfaces or background regions in the scene that otherwise lack sufficient texture for determining sufficiently dense 3D range data. In at least one embodiment, the 3D imaging apparatus provides for iterative or interactive imaging, such as by dynamically indicating that one or more regions of the scene require artificial texturing via the texture tool for accurate 3D imaging, and then indicating whether or not the addition of artificial texture cured the texture deficiency and/or whether any other regions within the image require additional texture.

Abstract: In one aspect, this disclosure presents a method and apparatus for verifying that minimum object contrast requirements are met within a region representing a volume to be monitored by a machine vision system. In complementary fashion, the disclosure also presents a methodology for constraining the positions of the lighting sources to be used for illuminating the monitored volume at a minimum height above the floor, and for the use of a key light that provides asymmetrical lighting within the monitored volume relative to the camera(s) used for imaging the monitored volume. Correspondingly, the disclosure also presents a method and apparatus for monitoring for proper operation of the key light and responding to improper operation. The minimum contrast verification and key light monitoring operations can be implemented using standalone apparatuses, or can be incorporated into the machine vision system.

Abstract: According to one aspect of the teachings presented herein, a machine vision system includes one or more sensor units that are each advantageously configured to use different pairings among a set of spaced-apart image sensors, to provide redundant object detection for a primary monitoring zone, while simultaneously providing for the detection of objects that may shadow the primary monitoring zone. Further, a plurality of “mitigations” and enhancements provide safety-of-design and robust operation. Such mitigations and enhancements include, for example, bad-pixel detection and mapping, cluster-based pixel processing for improved object detection, test-image injection for fault detection, dual-channel, redundant processing for safety-critical object detection, temporal filtering to reduce false detections, and the use of high dynamic range (HDR) images, for improved operation over variable ambient lighting conditions.

Abstract: The present invention comprises an apparatus and method for monitoring a zone, which is a contiguous or non-contiguous, two-dimensional (2D) area or three-dimensional (3D) volume. Among its several advantages, the apparatus includes a plurality of sensors that monitor portions of the zone and report intrusion status to a control unit that provides monitoring boundary information to each of the sensors based on user input and further “maps” or otherwise associates each sensor to control unit outputs in accordance with user-defined behaviors. Still further, as a meaningful aid for configuring monitoring boundaries used by the sensors for objection intrusion detection, in one or more embodiments of the apparatus and method, at least a subset of sensors use a common coordinate frame of reference, based on a common origin located within overlapping sensor fields of view.

Abstract: In one aspect of the teachings herein, a 3D imaging apparatus uses a “texture tool” or facilitates the use of such a tool by an operator, to add artificial texture to a scene being imaged, for 3D imaging of surfaces or background regions in the scene that otherwise lack sufficient texture for determining sufficiently dense 3D range data. In at least one embodiment, the 3D imaging apparatus provides for iterative or interactive imaging, such as by dynamically indicating that one or more regions of the scene require artificial texturing via the texture tool for accurate 3D imaging, and then indicating whether or not the addition of artificial texture cured the texture deficiency and/or whether any other regions within the image require additional texture.

Abstract: Zone selection logic in an area monitoring device increases the number of unique monitoring zone selections that can be selected using a limited number of discrete zone selection inputs, without compromising the safety critical operation of zone selection. The device monitors the logical state of each input in a set of individual inputs and recognizes each unique logical combination of zone selection inputs as a different zone selection. For example, each of two or more configured monitoring zones is associated with a different combinatorial zone selection pattern of asserted zone selection input signals. Correspondingly, a control circuit within the area monitoring device is configured to monitor the discrete zone selection inputs and activate a given one of the configured monitoring zones based on recognizing the associated combinatorial zone selection pattern of asserted zone selection signals.

Abstract: According to a method and apparatus taught herein an active object detection system performs reliable object detection based on light pulse emissions and corresponding and time-of-flight based distance determination, while advantageously rejecting clutter. While not limiting, the method and apparatus taught herein may be particularly advantageous for safety-critical object detection applications, such as where the active object detection system, e.g., a laser scanner, monitors for objects of at least a specified size within a predetermined monitoring radius or contour.

Abstract: Zone selection logic in an area monitoring device increases the number of unique monitoring zone selections that can be selected using a limited number of discrete zone selection inputs, without compromising the safety critical operation of zone selection. The device monitors the logical state of each input in a set of individual inputs and recognizes each unique logical combination of zone selection inputs as a different zone selection. For example, each of two or more configured monitoring zones is associated with a different combinatorial zone selection pattern of asserted zone selection input signals. Correspondingly, a control circuit within the area monitoring device is configured to monitor the discrete zone selection inputs and activate a given one of the configured monitoring zones based on recognizing the associated combinatorial zone selection pattern of asserted zone selection signals.

Abstract: According to a method and apparatus taught herein an active object detection system performs reliable object detection based on light pulse emissions and corresponding and time-of-flight based distance determination, while advantageously rejecting clutter. While not limiting, the method and apparatus taught herein may be particularly advantageous for safety-critical object detection applications, such as where the active object detection system, e.g., a laser scanner, monitors for objects of at least a specified size within a predetermined monitoring radius or contour.