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

IPC: G06T7/593 ,  G01S17/89

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.

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.

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.

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.