A system and method is disclosed for classifying one or more objects within a vicinity of a vehicle. Ultra-sonic data may be received from a plurality of ultra-sonic sensors and may comprise echo signals indicating one or more objects that are proximally located within a vicinity of a vehicle. One or more features may be calculated from the ultra-sonic data using one or more signal processing algorithms unique to each of the plurality of ultra-sonic sensors. The one more features may be combined using a second-level signal processing algorithm to determine geometric relations for the one or more objects. The one or more features may then be statistically aggregated at an object level. The one or more objects may then be classified using a machine learning algorithm that compares an input of each of the one or more features to a trained classifier.

A method of configuring a touch sensor includes transmitting an ultra-sonic test signal induced by a first excitation signal towards a touch structure that has a first interface with an enclosed interior volume of the touch sensor and a second interface with an external environment; receiving a plurality of ultra-sonic reflected signals produced from the ultra-sonic test signal and the touch structure, including a first ultra-sonic reflected signal internally reflected by the first interface and a last ultra-sonic reflected signal internally reflected by the second interface; determining a last time of flight corresponding to the last ultra-sonic reflected signal; and selectively configuring a second excitation signal based on the last time of flight. The second excitation signal is used for inducing further ultra-sonic signals.

An autonomous mobile device (AMD) uses sensors to explore a physical space and determine the locations of obstacles. Simultaneous localization and mapping (SLAM) techniques are used by the AMD to designate as keyframes some images and their associated descriptors of features in the space. Each keyframe indicates a location and orientation of the AMD relative to those features. Anchors are specified relative to keyframes. A marker is specified relative to one or more anchors. Because markers are associated with features in the physical space, they maintain their association with the physical space through various processes such as SLAM loop closures. Markers may specify locations in the physical space, such as navigation waypoints, navigation destinations such as a goal pose for exploring an unexplored area, as an observation target to facilitate exploration, and so forth. Markers may also be used to specify block listed locations to be avoided during exploration.

An apparatus for mobile machinery includes a sensor module configured for detachable fitment to mobile machinery, and a control module configured to interface with a control system of the mobile machinery. The sensor module includes an orthogonal sensor arrangement for sensing obstacles in three-dimensional space, an orthogonal indicator arrangement configured to provide a gradient proximity indication of an obstacle, and a wireless transceiver arranged in signal communication with the sensor and indicator arrangements. The control module includes a control wireless transceiver for communicating with the wireless transceiver of the sensor module, and a processor in signal communication with the control wireless transceiver, the processor configured to program, via the wireless transceiver of each sensor module, the sensor and indicator arrangements and with predetermined thresholds of obstacle proximity. If a sensed obstacle proximity exceeds a maximum threshold, the processor overrides the control system to facilitate situational awareness of an operator.

Techniques for calibrating presence-detection devices to account for various factors that can affect the presence-detection devices' ability to detect movement. Presence-detection devices may detect movement of a person in an environment by emitting ultrasonic signals into the environment, and characterizing the change in the frequency, or the Doppler shift, of the reflections of the ultrasonic signals off the person caused by the movement of the person. However, factors such as environmental acoustic conditions, noise sources, etc., may affect the ability of the presence-detection devices to detect movement. To calibrate for these factors, the presence-detection devices may use a loudspeaker to emit an ultrasonic sweep signal that spans different frequencies in an ultrasonic frequency range. The presence-detection devices may generate audio data using a microphone that represents the ultrasonic sweep signal, and analyze that audio data to determine an optimal frequency range and/or transmission power for subsequent ultrasonic signal transmissions.

Techniques for calibrating presence-detection devices to account for various factors that can affect the presence-detection devices' ability to detect movement. Presence-detection devices may detect movement of a person in an environment by emitting ultrasonic signals into the environment, and characterizing the change in the frequency, or the Doppler shift, of the reflections of the ultrasonic signals off the person caused by the movement of the person. However, factors such as environmental acoustic conditions, noise sources, etc., may affect the ability of the presence-detection devices to detect movement. To calibrate for these factors, the presence-detection devices may use a loudspeaker to emit an ultrasonic sweep signal that spans different frequencies in an ultrasonic frequency range. The presence-detection devices may generate audio data using a microphone that represents the ultrasonic sweep signal, and analyze that audio data to determine an optimal frequency range and/or transmission power for subsequent ultrasonic signal transmissions.

An ultrasonic-based person detection method. The method comprising the steps of: (a) emitting, from an emitter, an ultrasonic signal, the ultrasonic signal including a component at a first frequency; (b) receiving reflections of the ultrasonic signal, the received signal including components at frequencies greater than and less than the first frequency; (c) determining a difference between an upper portion of the received signal containing a frequency higher than the first frequency, and a lower portion of the received signal containing a frequency lower than the first frequency; and (d) determining, based on the difference between the upper portion and the lower portion, whether a person is present.

An ultrasonic-based person detection method. The method comprising the steps of: (a) emitting, from an emitter, an ultrasonic signal, the ultrasonic signal including a component at a first frequency; (b) receiving reflections of the ultrasonic signal, the received signal including components at frequencies greater than and less than the first frequency; (c) determining a difference between an upper portion of the received signal containing a frequency higher than the first frequency, and a lower portion of the received signal containing a frequency lower than the first frequency; and (d) determining, based on the difference between the upper portion and the lower portion, whether a person is present.

A work machine, light detection and ranging (LiDAR) system, and method of reducing false object detections are disclosed. The work machine may include a frame, a power unit, a locomotive device, and the LiDAR system. The LiDAR system may include at least one laser, at least one sensor, a bracket, and a control unit configured to execute an object detection program. The LiDAR system is configured to exclude from its object detection program a plurality of beams emitted toward the work machine through a design of the bracket and/or software methods of the control unit. The method may include storing a database of exclude coordinates and using the database to filter out undesirable data samples from the object detection program.

A work machine, light detection and ranging (LiDAR) system, and method of reducing false object detections are disclosed. The work machine may include a frame, a power unit, a locomotive device, and the LiDAR system. The LiDAR system may include at least one laser, at least one sensor, a bracket, and a control unit configured to execute an object detection program. The LiDAR system is configured to exclude from its object detection program a plurality of beams emitted toward the work machine through a design of the bracket and/or software methods of the control unit. The method may include storing a database of exclude coordinates and using the database to filter out undesirable data samples from the object detection program.