Uatc, LLC

Auris Health, Inc.

Auris Surgical Robotics, Inc.

Gehua Yang

David S Mintz

Prasanth Jeevan

Charles Vernon Stewart

Atiyeh Ghoreyshi

Yiliang Xu

Matthew Joseph Leotta

David McAllister Bradley

Michael Knoll Sergi-Curfman

Shantanu Jain

Methods and apparatuses provide improved navigation through tubular networks such as lung airways by providing improved estimation of location and orientation information of a medical instrument (e.g., an endoscope) within the tubular network. Various input data such as image data, EM data, and robot data are used by different algorithms to estimate the state of the medical instrument, and the state information is used to locate a specific site within a tubular network and/or to determine navigation information for what positions/orientations the medical instrument should travel through to arrive at the specific site. Probability distributions together with confidence values are generated corresponding to different algorithms are used to determine the medical instrument's estimated state.

Navigation of tubular networks

Aspects of the present disclosure involve a vehicle computer system comprising a computer-readable storage medium storing a set of instructions, and a method for light detection and ranging (Lidar) intensity calibration. The method includes collecting a data set comprising a plurality of raw intensity values output by a channel of a Lidar unit at a particular power level from among multiple power levels at which the channel is capable of operating. The method further includes using a linear model to compute a calibration multiplier and a bias value for the particular power level of the channel. During operation of the vehicle, calibrated intensity values are determined by applying the linear model to subsequent raw intensity values output by the channel at the particular power using the determined calibration multiplier and bias value.

Lidar intensity calibration

A LIDAR calibration module can detect a set of return signals from a plurality of fiducial targets for a set of laser scanners of an autonomous vehicle's LIDAR module. The autonomous vehicle can rest on an inclined platform of a rotating turntable to increase range variation in the return signals. Based on the set of return signals, the LIDAR calibration system can generate a set of calibration transforms to adjust a set of intrinsic parameters of the LIDAR module.

Calibration for an autonomous vehicle LIDAR module

A LIDAR calibration system can detect a first set of return signals from a plurality of fiducial targets in a calibration facility for a lower set of laser scanners of the LIDAR module. The LIDAR calibration system can also detect a second set of return signals from one or more planar surfaces associated with a calibration trigger location on a road network for an upper set of laser scanners of the LIDAR module. Based on the first and second sets of return signals, the LIDAR calibration system can generate a set of calibration transforms to adjust a set of intrinsic parameters of the LIDAR module.

Aspects of the present disclosure involve systems, methods, and devices for determining reflectance properties of objects based on Lidar intensity values, A system includes one or more processors of a machine and a machine-storage medium storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising accessing an incoming data point output by a Lidar unit during operation of a vehicle. The operations may further include inferring, using a reflectance inference model, a reflectance value of an object based on the incoming data point. The reflectance inference model comprises a mapping of previously collected data points to a coordinate system using associated range values and raw intensity values. The operations may further include determining one or more characteristics of the object based on the inferred reflectance value.

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Wexford, PA, United States of America

Gehua Yang