Nodar Inc.

Leaf Alden Jiang

Piotr Swierczynski

Philip Bradley Rosen

Carsten Louis Boers

Jing Wang

Matthew Beveridge

Vikram Shree

An automatically calibrated vision system includes: a vision calibration system; a first sensor system that receives first data of a scene captured by a first sensor on a movable machine and outputs a first map based on the first data; a second sensor system that receives second data of the scene captured by a second sensor on the movable machine and outputs a second map based on the second data. The vision calibration system computes calibration data based on the first and second maps, supplies the calibration data to the first sensor system and/or the second sensor system for automatic calibration of the first sensor system and/or the second sensor system, and outputs, to a controller of the movable machine, a calibrated depth map comprised of depth measurements calibrated using the calibration data. The first sensor system and/or the second sensor system automatically perform(s) a self-calibration using the calibration data.

3D vision system with automatically calibrated stereo vision sensors and lidar sensor

A long-baseline and long depth-range stereo vision system is provided that is suitable for use in non-rigid assemblies where relative motion between two or more cameras of the system does not degrade estimates of a depth map. The stereo vision system may include a processor that tracks camera parameters as a function of time to rectify images from the cameras even during fast and slow perturbations to camera positions. Factory calibration of the system is not needed, and manual calibration during regular operation is not needed, thus simplifying manufacturing of the system.

Systems and methods for object identification with a calibrated stereo-vision system

An automated vehicle assistance system is provided for supervised or unsupervised vehicle movement. The system includes a control system and a first sensor system. The first sensor system may receive first image data of a scene and may output a first disparity map and a first confidence map based on the first image data. The control system may output a video stream based on the first disparity map and the first confidence map. The vehicle assistance system also may include a second sensor system that receives second image data of at least a portion of the scene that outputs a second confidence map based on second image data. The video stream may include super-frames, with each super-frame including a 2D image of the scene, a depth map corresponding to the 2D image, and a certainty map corresponding to the depth map.

Methods and systems for providing depth maps with confidence estimates

A method of detecting and tracking objects includes (a) obtaining point-cloud data captured by camera sensors on a vehicle travelling along a road surface, (b) compressing the point-cloud data to bird's-eye-view (BEV) data representing a view along a BEV direction oriented approximately perpendicular to the road surface, the BEV data representing the objects on a BEV plane oriented approximately parallel the road surface, (c) establishing a grid of cells for the BEV plane, (d) for each cell, determining occupancy values of the cell for a time tand performing noise reduction by assigning weights to the cell based on predicted occupancy values of the cell for the time tand on occupancy values of the cell for a previous time t, and (e) outputting to a controller of the vehicle, in real time or nearly real time, an occupancy evidence map of objects in the vehicle's environment.

Stereo vision system and method for small-object detection and tracking in real time

A system and a method are provided for repairing a depth map. The repairing includes representing pixels of the depth map using a matrix H having values corresponding to depths, the pixels including hole pixels and non-hole pixels; determining a confidence mask matrix C for the depth map; for each hole pixel, determining a kernel G of neighboring pixels, the neighboring pixels having values corresponding to a predetermined distribution, and convolving the kernel G with corresponding portions of the matrix C and the matrix H to obtain an estimated depth; generating a corrected matrix Ĥ in which values for non-hole pixels are retained and in which the hole pixels have values corresponding to their estimated depths. The estimated depths may be normalized based on the matrix C. An updated depth map based on the corrected matrix Ĥ may be outputted to a control system of a vehicle.

Real-time confidence-based image hole-filling for depth maps

A small-object perception system, for use in a vehicle, includes a stereo vision system that captures stereo images and outputs information identifying an object having a dimension in a range of ˜20 cm to about ˜100 cm in a perception range of ˜3 meters to ˜150 meters from the vehicle, and a system controller configured to receive output signals from the stereo vision system and to provide control signals to control a path of movement of the vehicle. The stereo vision system includes cameras separated by a baseline of ˜1 meter to ˜4 meters. The stereo vision system includes a stereo matching module configured to perform stereo matching on left and right initial images and to output a final disparity map based on a plurality of preliminary disparity maps generated from the left and right initial images, with the preliminary disparity maps having different resolutions from each other.

Detection of near-range and far-range small objects for autonomous vehicles

Stereo vision camera system that tracks and filters calibration parameters

3D vision system with automatically calibrated stereo vision sensors and LiDAR sensor

A small-object perception system, for use in a vehicle, includes a stereo vision system that captures stereo images and outputs information identifying an object having a dimension in a range of &#x2dc;20 cm to about &#x2dc;100 cm in a perception range of &#x2dc;3 meters to &#x2dc;150 meters from the vehicle, and a system controller configured to receive output signals from the stereo vision system and to provide control signals to control a path of movement of the vehicle. The stereo vision system includes cameras separated by a baseline of &#x2dc;1 meter to &#x2dc;4 meters. The stereo vision system includes a stereo matching module configured to perform stereo matching on left and right initial images and to output a final disparity map based on a plurality of preliminary disparity maps generated from the left and right initial images, with the preliminary disparity maps having different resolutions from each other.

Growing community of inventors

Concord, MA, United States of America

Leaf Alden Jiang