Abstract: Techniques related to indirect sparse simultaneous localization and mapping (SLAM) are discussed. Such techniques include adaptively positioning a virtual camera relative to an estimated position of a physical camera within an environment to be mapped, projecting a depth error to an image plane corresponding to the adaptive camera position, and using the projected depth error to update a mapping of the environment.

Abstract: An example apparatus for selecting keypoints in image includes a keypoint detector to detect keypoints in a plurality of received images. The apparatus also includes a score calculator to calculate a keypoint score for each of the detected keypoints based on a descriptor score indicating descriptor invariance. The apparatus includes a keypoint selector to select keypoints based on the calculated keypoint scores. The apparatus also further includes a descriptor calculator to calculate descriptors for each of the selected keypoints. The apparatus also includes a descriptor matcher to match corresponding descriptors between images in the plurality of received images. The apparatus further also includes a feature tracker to track a feature in the plurality of images based on the matched descriptors.

Abstract: Techniques related to indirect sparse simultaneous localization and mapping (SLAM) are discussed. Such techniques include adaptively positioning a virtual camera relative to an estimated position of a physical camera within an environment to be mapped, projecting a depth error to an image plane corresponding to the adaptive camera position, and using the projected depth error to update a mapping of the environment.

Abstract: An embodiment of an image processor device includes technology to fetch a feature point data set from outside a local memory, locally store three or more fetched feature point data sets in the local memory, compute orientation information for each fetched feature point data set, compute first descriptor information based on the computed orientation information and a first locally stored feature point data set in parallel with a fetch and local store of a second feature point data set in the local memory, and compute second descriptor information based on the computed orientation information and the second locally stored feature point data set in parallel with the compute of the first descriptor information. Other embodiments are disclosed and claimed.

Abstract: An example apparatus for tracking features in image data includes an image data receiver to receive initial image data corresponding to an image from a camera and store the image data a circular buffer. The apparatus also includes a feature detector to detect features in the image data. The apparatus further includes a feature sorter to sort the detected features to generate sorted feature points. The apparatus includes a feature tracker to track the sorted feature points in subsequent image data corresponding to the image received at the image data receiver. The subsequent image data is to replace the initial image data in the circular buffer.

Abstract: Techniques related to indirect sparse simultaneous localization and mapping (SLAM) are discussed. Such techniques include adaptively positioning a virtual camera relative to an estimated position of a physical camera within an environment to be mapped, projecting a depth error to an image plane corresponding to the adaptive camera position, and using the projected depth error to update a mapping of the environment.

Abstract: An example apparatus for tracking features in image data includes an image data receiver to receive initial image data corresponding to an image from a camera and store the image data a circular buffer. The apparatus also includes a feature detector to detect features in the image data. The apparatus further includes a feature sorter to sort the detected features to generate sorted feature points. The apparatus includes a feature tracker to track the sorted feature points in subsequent image data corresponding to the image received at the image data receiver. The subsequent image data is to replace the initial image data in the circular buffer.

Abstract: An apparatus, method, and computer readable medium to accommodate depth noise in SLAM (Simultaneous Localization & Mapping). The method includes receiving correspondences and clusters for pose estimation. After the correspondences and clusters are received, a dynamic centroid is determined using 3-D features of a landmark. The 3-D features of the landmark comprise a cluster. Next, distance-error metrics are determined using the dynamic centroid, a map point, and the cluster. The distance-error metrics are compared with thresholds to remove depth noise affected 3-D features and landmarks when the distance-error metrics are larger than the thresholds. The remaining 3-D features of the cluster are sent to a pose estimation framework.

Abstract: Techniques related to indirect sparse simultaneous localization and mapping (SLAM) are discussed. Such techniques include adaptively positioning a virtual camera relative to an estimated position of a physical camera within an environment to be mapped, projecting a depth error to an image plane corresponding to the adaptive camera position, and using the projected depth error to update a mapping of the environment.

Abstract: An embodiment of an image processor device includes technology to fetch a feature point data set from outside a local memory, locally store three or more fetched feature point data sets in the local memory, compute orientation information for each fetched feature point data set, compute first descriptor information based on the computed orientation information and a first locally stored feature point data set in parallel with a fetch and local store of a second feature point data set in the local memory, and compute second descriptor information based on the computed orientation information and the second locally stored feature point data set in parallel with the compute of the first descriptor information. Other embodiments are disclosed and claimed.

Abstract: An apparatus, method, and computer readable medium to accommodate depth noise in SLAM (Simultaneous Localization & Mapping). The method includes receiving correspondences and clusters for pose estimation. After the correspondences and clusters are received, a dynamic centroid is determined using 3-D features of a landmark. The 3-D features of the landmark comprise a cluster. Next, distance-error metrics are determined using the dynamic centroid, a map point, and the cluster. The distance-error metrics are compared with thresholds to remove depth noise affected 3-D features and landmarks when the distance-error metrics are larger than the thresholds. The remaining 3-D features of the cluster are sent to a pose estimation framework.

Abstract: One embodiment provides an image processing circuitry. The image processing circuitry includes a feature extraction circuitry and an optimization circuitry. The feature extraction circuitry is to determine a feature descriptor based, at least in part, on a feature point location and a corresponding scale. The optimization circuitry is to optimize an operation of the feature extraction circuitry. Each optimization is to at least one of accelerate the operation of the feature extraction circuitry, reduce a power consumption of the feature extraction circuitry and/or reduce a system memory bandwidth used by the feature extraction circuitry.

Abstract: An example apparatus for selecting keypoints in image includes a keypoint detector to detect keypoints in a plurality of received images. The apparatus also includes a score calculator to calculate a keypoint score for each of the detected keypoints based on a descriptor score indicating descriptor invariance. The apparatus includes a keypoint selector to select keypoints based on the calculated keypoint scores. The apparatus also further includes a descriptor calculator to calculate descriptors for each of the selected keypoints. The apparatus also includes a descriptor matcher to match corresponding descriptors between images in the plurality of received images. The apparatus further also includes a feature tracker to track a feature in the plurality of images based on the matched descriptors.

Abstract: An example apparatus for tracking features in image data includes an image data receiver to receive initial image data corresponding to an image from a camera and store the image data a circular buffer. The apparatus also includes a feature detector to detect features in the image data. The apparatus further includes a feature sorter to sort the detected features to generate sorted feature points. The apparatus includes a feature tracker to track the sorted feature points in subsequent image data corresponding to the image received at the image data receiver. The subsequent image data is to replace the initial image data in the circular buffer.

Abstract: One embodiment provides an image processing circuitry. The image processing circuitry includes a feature extraction circuitry and an optimization circuitry. The feature extraction circuitry is to determine a feature descriptor based, at least in part, on a feature point location and a corresponding scale. The optimization circuitry is to optimize an operation of the feature extraction circuitry. Each optimization is to at least one of accelerate the operation of the feature extraction circuitry, reduce a power consumption of the feature extraction circuitry and/or reduce a system memory bandwidth used by the feature extraction circuitry.

Abstract: Aspects of the present disclosure relates to technologies (systems, devices, methods, etc.) for performing feature detection and/or feature tracking based on image data. In embodiments, the technologies include or leverage a SLAM hardware accelerator (SWA) that includes a feature detection component and optionally a feature tracking component. The feature detection component may be configured to perform feature detection on working data encompassed by a sliding window. The feature tracking component is configured to perform feature tracking operations to track one or more detected features, e.g., using normalized cross correlation (NCC) or another method.