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公开(公告)号：US20240418820A1
公开(公告)日：2024-12-19
申请号：US18391512
申请日：2023-12-20
Applicant: Torc Robotics, Inc.
Inventor： Felix Heide , Jim Aldon D'Souza
Abstract: An autonomous vehicle including a microphone array of a plurality of microphones, a visual sensor network configured to receive visual signals, at least one processor, and at least one memory storing instructions is disclosed. The instructions, when executed by the at least one processor, cause the at least one processor to: (i) generate spatial beamforming maps locating a sound source using a beamforming model corresponding to acoustic signals received at the plurality of microphones of the microphone array; (ii) apply a synthetic aperture expansion to the acoustic signals to increase resolution of the spatial beamforming maps; and (iii) generate feature maps for an application in autonomous vehicle driving by combining the improved spatial beamforming maps with visualization maps generated based on the visual signals received by the visual sensor network.
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公开(公告)号：US20240422502A1
公开(公告)日：2024-12-19
申请号：US18391537
申请日：2023-12-20
Applicant: Torc Robotics, Inc.
Inventor： Felix Heide , Jim Aldon D'Souza
IPC: H04S7/00
Abstract: An autonomous vehicle including a network of sensors including a plurality of acoustic sensors and a plurality of visual sensors, at least one processor, and at least one memory storing instructions is disclosed. The instructions, when executed by the at least one processor, cause the at least one processor to: (i) generate spatial beamforming maps locating a sound source based upon acoustic signals received at the plurality of acoustic sensors; (ii) identify a type of an object generating the acoustic signals received at the plurality of acoustic sensors based upon comparison of the acoustic signals with a plurality of acoustic signals and respective objects stored in a dataset; and (iii) generate feature maps for an application in an autonomous vehicle driving by enhancing visualization maps generated based upon visual signals received by the plurality of visual sensors.
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公开(公告)号：US20240416953A1
公开(公告)日：2024-12-19
申请号：US18391527
申请日：2023-12-20
Applicant: Torc Robotics, Inc.
Inventor： Felix Heide , Jim Aldon D'Souza
Abstract: An autonomous vehicle including a microphone array of a plurality of microphones, a visual sensor network configured to receive visual signals, at least one processor, and at least one memory storing instructions is disclosed. The instructions, when executed by the at least one processor, cause the at least one processor to: (i) generate spatial beamforming maps locating a sound source using a beamforming model corresponding to acoustic signals received at the plurality of microphones of the microphone array; (ii) apply a synthetic aperture expansion to the acoustic signals to increase resolution of the spatial beamforming maps; and (iii) generate a future visual frame based at least partially upon temporal information extracted from the spatial beamforming maps and visualization maps generated based on the visual signals received by the visual sensor network.
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公开(公告)号：US20240420356A1
公开(公告)日：2024-12-19
申请号：US18743696
申请日：2024-06-14
Applicant: Torc Robotics, Inc.
Inventor： Felix Heide , Fahim Mannan , Mario Bijelic
Abstract: A perception system including at least one memory, and at least one processor configured to: (i) compute, in a stereo branch, disparity from a pair of stereo images including a left image and a right image; (ii) based on the computed disparity from the pair of stereo images, output, by the stereo branch, a depth for the left image and a depth for the right image; (iii) compute an absolute depth for the left image in a first monocular branch and an absolute depth for the right image in a second monocular branch; (iv) compute, in a first fusion branch, a depth map for the left image; (v) compute, in a second fusion branch, a depth map for the right image; and (vi) generate a single fused depth map based on the depth map for the left image and the depth map for the right image, is disclosed.
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公开(公告)号：US20240331366A9
公开(公告)日：2024-10-03
申请号：US18526787
申请日：2023-12-01
Applicant: Torc Robotics, Inc.
Inventor： Emmanuel Luc Julien Onzon , Felix Heide , Maximilian Rufus Bömer , Fahim Mannan
CPC classification number: G06V10/776 , G06T5/40 , G06T5/50 , G06T7/11 , G06V10/7715 , G06V10/806 , G06V10/955 , G06V20/38 , G06T2207/10144 , G06T2207/20161
Abstract: A computer-vision pipeline is organized as a closed loop of a sensor-processing phase, an image-processing phase, and an object-detection phase, each comprising a respective phase processor coupled to a master processor. The sensor-processing phase creates multiple exposure images, and derives multi-exposure multi-scale zonal illumination-distributions, to be processed independently in the image-processing phase. In a first implementation of the object-detection phase, extracted exposure-specific features are pooled prior to overall object detection. In a second implementation, exposure-specific objects, detected from the exposure-specific features, are fused to produce the sought objects of a scene under consideration. The two implementations enable detecting fine details of a scene under diverse illumination conditions. The master processor performs loss-function computations to derive updated training parameters of the processing phases. Several experiments applying a core method of operating the computer-vision pipelines, and variations thereof, ascertain performance gain under challenging illumination conditions.
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Patent Agency Ranking
公开(公告)号：US20240418860A1
公开(公告)日：2024-12-19
申请号：US18743717
申请日：2024-06-14
Applicant: Torc Robotics, Inc.
Inventor： Felix Heide , Mario Bijelic , Nicolas Robidoux
IPC: G01S17/894 , G01S7/481 , G01S7/4863 , G01S17/931 , G06T7/73
Abstract: A system including at least one memory and at least one processor configured to: (i) identify a set of hyperparameters affecting a wavefront and a pipeline processing a signal corresponding to a pulse received at a detector of a light detection and ranging (LiDAR) sensor; (ii) identify a set of 3-dimensional (3D) objects for detection using a neural network with the set of hyperparameters optimized based at least in part on a Covariance Matrix Adaptation-Evolution Strategy (CMA-ES) and a square root of covariance matrix scale factor; (iii) detect the set of 3D objects from a plurality of LiDAR point clouds using the neural network with the optimized set of hyperparameters and using a manually tuned set of hyperparameters; and (iv) validate the neural network optimized set of hyperparameters and the manually tuned set of hyperparameters using an average precision based upon the detected set of 3D objects, is disclosed.
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公开(公告)号：US20240418839A1
公开(公告)日：2024-12-19
申请号：US18743546
申请日：2024-06-14
Applicant: Torc Robotics, Inc.
Inventor： Felix Heide , Mario Bijelic , Nicolas Robidoux
IPC: G01S7/487 , G01S7/497 , G01S17/89 , G01S17/931
Abstract: A system including at least one memory storing instructions, and at least one processor in communication with the at least one memory is disclosed. The at least one processor is configured to execute the stored instructions to: (i) control a light detection and ranging (LiDAR) sensor to emit a pulse into an environment of the LiDAR sensor; (ii) generate temporal histograms corresponding to a signal detected by a detector of the LiDAR sensor for the pulse emitted by the LiDAR sensor; (iii) denoise a temporal waveform generated based on the temporal histograms; (iv) estimate ambient light; (v) determine a noise threshold corresponding to the ambient light; (vi) determine a peak of a plurality of peaks that has a maximum intensity; and (vii) add the peak to a point cloud.
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公开(公告)号：US20240418837A1
公开(公告)日：2024-12-19
申请号：US18743628
申请日：2024-06-14
Applicant: Torc Robotics, Inc.
Inventor： Felix Heide , Mario Bijelic , Nicolas Robidoux
IPC: G01S7/4863 , G01S17/894 , G01S17/931
Abstract: A system including at least one memory storing instructions, and at least one processor in communication with the at least one memory is disclosed. The at least one processor is configured to execute the stored instructions to: (i) initiate optimization of a pulse emitted by a light detection and ranging (LiDAR) sensor into an environment of the LiDAR sensor using a respective channel of a plurality of channels of the LiDAR sensor; (ii) initiate optimization of a pipeline processing a signal corresponding to the emitted pulse received at a detector of the LiDAR sensor; (iii) construct a max-rank loss scalarization for the signal using the optimized pipeline; (iv) compute transients using centroid weights based upon the max-rank loss scalarization; and (v) replace a centroid based upon a covariance matrix adaptation-evolution strategy (CMA-ES) upon determining a new centroid corresponding to the computed transients.
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9.发明公开公开(公告)号：US20240233351A1
公开(公告)日：2024-07-11
申请号：US18545874
申请日：2023-12-19
Applicant: Torc Robotics, Inc.
Inventor： Emmanuel Luc Julien Onzon , Felix Heide , Maximilian Rufus Bömer , Fahim Mannan
CPC classification number: G06V10/806 , G06T5/70 , G06V10/60 , G06V10/82
Abstract: Departing from conventional HIDR image fusion approach, a learned task-driven fusion in the feature domain is disclosed. Instead of using a single companded image, the disclosed method exploits semantic features from all exposures learned in an end-to-end fashion with supervision from downstream detection losses. The method outperforms all tested conventional HDR exposure fusion and auto-exposure methods in challenging automotive HIDR scenarios.
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10.发明公开公开(公告)号：US20240127584A1
公开(公告)日：2024-04-18
申请号：US18526787
申请日：2023-12-01
Applicant: Torc Robotics, Inc.
Inventor： Emmanuel Luc Julien Onzon , Felix Heide , Maximilian Rufus Bömer , Fahim Mannan
CPC classification number: G06V10/776 , G06T5/40 , G06T5/50 , G06T7/11 , G06V10/7715 , G06V10/806 , G06V10/955 , G06V20/38 , G06T2207/10144 , G06T2207/20161
Abstract: A computer-vision pipeline is organized as a closed loop of a sensor-processing phase, an image-processing phase, and an object-detection phase, each comprising a respective phase processor coupled to a master processor. The sensor-processing phase creates multiple exposure images, and derives multi-exposure multi-scale zonal illumination-distributions, to be processed independently in the image-processing phase. In a first implementation of the object-detection phase, extracted exposure-specific features are pooled prior to overall object detection. In a second implementation, exposure-specific objects, detected from the exposure-specific features, are fused to produce the sought objects of a scene under consideration. The two implementations enable detecting fine details of a scene under diverse illumination conditions. The master processor performs loss-function computations to derive updated training parameters of the processing phases. Several experiments applying a core method of operating the computer-vision pipelines, and variations thereof, ascertain performance gain under challenging illumination conditions.
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