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    System for generating a navigational map of an environment
    1.
    发明授权
    System for generating a navigational map of an environment 有权

    公开(公告)号：US11321914B1

    公开(公告)日：2022-05-03

    申请号：US15867449

    申请日：2018-01-10

    Applicant: AMAZON TECHNOLOGIES, INC.

    Inventor： David Allen Fotland ,  Roger Robert Webster ,  Arif Dikici

    IPC: B25J9/16 ,  G06T17/05 ,  B25J9/00 ,  G06T7/73

    Abstract: A point cloud or map of an environment is generated by determining sets of landmark points from multiple images, such as through use of a Simultaneous Localization and Mapping (SLAM) algorithm. Images acquired using a depth camera are used to determine depth points indicative of at least one object not represented by the landmark points. A combined map is generated to include both the landmark points and the depth points. The set of depth points is mapped to a corresponding set of landmark points based on proximity of the points, similarity of the camera poses, or times that the images were acquired. The relationship between the depth and landmark points may be determined. When the landmark points are moved, such as to account for error in the SLAM algorithm, the depth points may be moved to a modified location relative to the landmark points so that the relationship remains constant.

    Motion detection
    3.
    发明授权
    Motion detection 有权

    公开(公告)号：US10200572B1

    公开(公告)日：2019-02-05

    申请号：US15060491

    申请日：2016-03-03

    Applicant: Amazon Technologies, Inc.

    Inventor： David Allen Fotland ,  Alexander Michael Fotland

    IPC: H04N19/137 ,  H04N5/14

    Abstract: Various examples are directed to systems and methods for detection motion in video data. An image processor may receive video data. A first frame and a second frame may describe a plurality of pixel locations. The second frame may be positioned after the first frame in the frame sequence. The image processor may determine that a first subset of pixel locations from the plurality of pixel locations differ by less than a threshold amount between frames for at least a threshold number of frames in the frame sequence prior to the first frame. The image processor may identify from the first subset of pixel locations, a second subset of pixel locations that differ by more than the threshold amount between the first frame and the second frame and determine that a number of pixel locations in the second subset of pixel locations is greater than a motion pixel threshold.

    MULTI-TRACKER OBJECT TRACKING
    4.
    发明申请
    MULTI-TRACKER OBJECT TRACKING 有权
    多跟踪器对象跟踪

    公开(公告)号：US20150055821A1

    公开(公告)日：2015-02-26

    申请号：US13973913

    申请日：2013-08-22

    Applicant: Amazon Technologies, Inc.

    Inventor： David Allen Fotland

    IPC: G06K9/32 ,  G06K9/00

    CPC classification number: G06K9/3241 ,  G06K9/00228 ,  G06K9/00369 ,  G06K2009/3291 ,  G06T7/246 ,  G06T2207/10016 ,  G06T2207/30201

    Abstract: Systems and approaches are provided for tracking an object using multiple tracking processes. By combining multiple lightweight tracking processes, object tracking can be robust, use a limited amount of power, and enable a computing device to respond to input corresponding to the motion of the object in real time. The multiple tracking processes can be run in parallel to determine the position of the object by selecting the results of the best performing tracker under certain heuristics or combining the results of multiple tracking processes in various ways. Further, other sensor data of a computing device can be used to improve the results provided by one or more of the tracking processes.

    Abstract translation: 提供了使用多个跟踪过程跟踪对象的系统和方法。 通过组合多个轻量级跟踪过程，对象跟踪可以是鲁棒的，使用有限的功率，并且使得计算设备能够实时响应对象的运动的输入。 可以并行运行多个跟踪过程，以通过在某些启发式下选择最佳性能的跟踪器的结果或以多种方式组合多个跟踪过程的结果来确定对象的位置。 此外，可以使用计算设备的其他传感器数据来改善由一个或多个跟踪处理提供的结果。

    System to determine stationary features by autonomous mobile device
    8.
    发明授权
    System to determine stationary features by autonomous mobile device 有权

    公开(公告)号：US11442463B1

    公开(公告)日：2022-09-13

    申请号：US16579059

    申请日：2019-09-23

    Applicant: AMAZON TECHNOLOGIES, INC.

    Inventor： David Allen Fotland ,  Ishan Ankur Patil ,  Yan Lu ,  Joydeep Biswas

    IPC: G05D1/02 ,  G05D1/00 ,  G06T7/10 ,  G06T7/73 ,  G06V20/58

    Abstract: An autonomous mobile device moves through a physical space using simultaneous localization and mapping (SLAM) techniques. SLAM processes images from cameras to determine localization and trajectory of the device based on features that are assumed to be stationary. SLAM performance is improved by removing moving features from consideration. A first position of a feature at a first time and data from an inertial sensor are used to determine a predicted position at a second time. The predicted position is compared to a second position of the feature at the second time. This comparison takes into consideration an assumed Gaussian error distribution of how the positions are determined. If the predicted position differs from the second position by less than a threshold value, the feature may be determined to be stationary. The stationary features are then processed using SLAM to determine the localization and trajectory information.

    Identifying items in images using regions-of-interest
    9.
    发明授权
    Identifying items in images using regions-of-interest 有权

    公开(公告)号：US10007860B1

    公开(公告)日：2018-06-26

    申请号：US14976696

    申请日：2015-12-21

    Applicant: Amazon Technologies, Inc.

    Inventor： David Allen Fotland ,  Ambrish Tyagi

    IPC: G06K9/00 ,  G06K9/32 ,  G06K9/62 ,  G06T7/00 ,  G06K9/46

    CPC classification number: G06K9/3233 ,  G06K9/00369 ,  G06T7/11 ,  G06T7/187 ,  G06T7/194 ,  G06T2207/20132

    Abstract: The techniques described herein may identify images that likely depict one or more items by comparing features of the items to features of different regions-of-interest (ROIs) of the images. For instance, some of the images may include a user, and the techniques may define multiple regions within the image corresponding to different portions of the user. The techniques may then use a trained convolutional neural network or any other type of trained classifier to determine, for each region of the image, whether the region depicts a particular item. If so, the techniques may designate the corresponding image as depicting the item and may output an indication that the image depicts the item. The techniques may perform this process for multiple images, outputting an indication of each image deemed to depict the particular item.

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