Abstract: Embodiments relate to hazard detection in autonomous and semi-autonomous systems and applications. A transformer may use sampled image and LiDAR features to extract and decode a representation of one or more features of each point (e.g., refined height, range, driving condition, etc.) on a sampled surface (e.g., the road). These detections may be provided to one or more control components of an autonomous vehicle, which may use the detections to navigate, plan, or otherwise perform one or more operations. Some embodiments employ an automated approach to derive ground truth data from sensor data collected by data collection vehicle(s), such as data representing detected ground surface models, detected surface features, detected weather and/or surface condition labels, and/or detected per-point artifact labels. Accordingly, surface features such as ground surface heights along a predicted trajectory may be detected and ground truth data may be generated for a variety of sensing tasks.

Abstract: Embodiments relate to hazard detection in autonomous and semi-autonomous systems and applications. A transformer may use sampled image and LiDAR features to extract and decode a representation of whether there is a hazard at the 3D location corresponding to each initial transformer query, the shape of the hazard, and/or its class. These detections may be provided to one or more control components of an autonomous vehicle, which may use the detections to navigate, plan, or otherwise perform one or more operations (e.g., obstacle avoidance, lane keeping, lane changing, merging, splitting, etc.). Some embodiments employ an automated approach to derive ground truth data from sensor data collected by data collection vehicle(s), such as data representing detected static scene points, navigable space boundaries, or detected hazard objects. Accordingly, hazards such as road debris and other obstacles may be detected and ground truth data may be generated for a variety of sensing tasks.

Abstract: In various examples, optical flow-based algorithms may be used to detect objects in an environment by computing displacement fields for images captured using asynchronous cameras. As an example, an asynchronous set of cameras (e.g., two or more cameras) may capture a series of asynchronous images of an environment. Additionally, in some examples, the cameras may be positioned at different locations and capture different fields of view of the environment. Based at least on the differing image capture times and/or the differing fields of view of the images, image pixels corresponding to the same, physical locations in the environment may move locations between images of the series of images. The disclosed systems and methods may use optical flow algorithms to compute scores associated with the displacement/movement of the pixels throughout the series of images, as well as use these scores to detect objects in the environment.

Abstract: Embodiments of the present disclosure relate to correction of LiDAR measurement bias. In some embodiments, a LiDAR measurement bias such as a range-dependent height offset and/or a reflectivity-dependent height offset may be estimated in an offline process, the estimated biases may be stored in any suitable way (e.g., in one or more look up tables, indexed by range and/or reflectivity), and LiDAR points measured during an online process may be compensated by looking up and subtracting a range-dependent height bias corresponding to the measured range, and/or by looking up and subtracting a reflectivity-dependent height bias corresponding to the measured reflectivity.

Abstract: Embodiments of the present disclosure relate to ground surface estimation using localized surface fitting. A three-dimensional (3D) surface structure (e.g., a road surface profile) may be estimated using a nonlinear optimization to fit height values to (e.g., accumulated, bias-corrected) LiDAR detections (e.g., sampled in localized regions along one or more predicted trajectories). For example, LiDAR data (e.g., detected 3D point clouds) may be ego-motion compensated, corrected for measurement bias, accumulated, and sampled along one or more predicted trajectories, and the height of each trajectory point may be fitted to the heights of the corresponding sampled points using a nonlinear optimization. As such, the resulting road surface profile (e.g., modeled along the wheel track(s)) may be provided to an adaptive suspension control system to modulate the damping characteristic of the suspension system to counteract indentations (e.g., potholes) or protrusions (e.g.

Abstract: Embodiments of the present disclosure relate to surface estimation using stereo imaging and surface disparities. For example, a three-dimensional (3D) surface structure may be modeled as a disparity field, and a surface disparity field representing a surface in the environment (e.g., the ground) may be generated using a constrained nonlinear hierarchical optimization to process stereo image data and iteratively refine estimated surface disparity values based on weights that guide the optimization to expected surface values (e.g., ground, road). The resulting surface (e.g., ground) disparity field may be used for a variety of downstream tasks, such as obstacle detection, segmentation of a navigable space, ego-motion refinement, and/or generation of an estimated surface profile.

Abstract: Embodiments of the present disclosure relate to surface estimation using stereo imaging and surface disparities. For example, a surface disparity field representing a surface in the environment (e.g., the ground) may be estimated from stereo image data and used for various downstream tasks. For example, the difference between a stereo disparity field and a ground disparity field may be used to detect objects, a representation of a navigable space may be generated by radially casting 2D rays in the ground disparity field, the ground disparity field may be used to compensate ego-motion for high dynamic attitude changes, and/or the ground disparity field may be lifted to 3D and used to fit a surface profile to points sampled from the lifted point cloud.

Abstract: Embodiments relate to hazard detection in autonomous and semi-autonomous systems and applications. A transformer may use sampled image and LiDAR features to extract and decode a representation of whether there is a hazard at the 3D location corresponding to each initial transformer query, the shape of the hazard, and/or its class. These detections may be provided to one or more control components of an autonomous vehicle, which may use the detections to navigate, plan, or otherwise perform one or more operations (e.g., obstacle avoidance, lane keeping, lane changing, merging, splitting, etc.). Some embodiments employ an automated approach to derive ground truth data from sensor data collected by data collection vehicle(s), such as data representing detected static scene points, navigable space boundaries, or detected hazard objects. Accordingly, hazards such as road debris and other obstacles may be detected and ground truth data may be generated for a variety of sensing tasks.

Abstract: Embodiments relate to hazard detection in autonomous and semi-autonomous systems and applications. A transformer may use sampled image and LiDAR features to extract and decode a representation of one or more features of each point (e.g., refined height, range, driving condition, etc.) on a sampled surface (e.g., the road). These detections may be provided to one or more control components of an autonomous vehicle, which may use the detections to navigate, plan, or otherwise perform one or more operations. Some embodiments employ an automated approach to derive ground truth data from sensor data collected by data collection vehicle(s), such as data representing detected ground surface models, detected surface features, detected weather and/or surface condition labels, and/or detected per-point artifact labels. Accordingly, surface features such as ground surface heights along a predicted trajectory may be detected and ground truth data may be generated for a variety of sensing tasks.

Abstract: A mobile calibration room may be used for calibrating one or more sensors used on unmanned aerial vehicles (UAVs). A system can include folding or collapsible walls to enable the system to be moved between a stowed position and a deployed position. In the deployed position, the system can comprise a calibration room including one or more 2D or 3D targets used to calibrate one or more sensors (e.g., cameras) on a UAV. The system can include a turntable to rotate the UAV about a first axis during calibration. The system can also include a cradle to rotate the UAV around, or translate the UAV along, a second axis. The turntable can include a frame to rotate the UAV around a third axis during calibration. The mobile calibration room can be coupled to a vehicle to enable the mobile calibration room to be moved between locations.

Abstract: Examples of the present disclosure describe systems and methods for capturing data to acquire indoor and outdoor geometry. In aspects, a data capture system may be configured to acquire texture data, geometry data, navigation data and/or orientation data to support geolocation and georeferencing within indoor and outdoor environments. The data capture system may further be configured to acquire seamless texture data from a 360° horizontal and vertical perspective to support panoramic video and images.

Abstract: A mobile calibration room may be used for calibrating one or more sensors used on unmanned aerial vehicles (UAVs). A system can include folding or collapsible walls to enable the system to be moved between a stowed position and a deployed position. In the deployed position, the system can comprise a calibration room including one or more 2D or 3D targets used to calibrate one or more sensors (e.g., cameras) on a UAV. The system can include a turntable to rotate the UAV about a first axis during calibration. The system can also include a cradle to rotate the UAV around, or translate the UAV along, a second axis. The turntable can include a frame to rotate the UAV around a third axis during calibration. The mobile calibration room can be coupled to a vehicle to enable the mobile calibration room to be moved between locations.

Abstract: Examples of the present disclosure describe systems and methods for capturing data to acquire indoor and outdoor geometry. In aspects, a data capture system may be configured to acquire texture data, geometry data, navigation data and/or orientation data to support geolocation and georeferencing within indoor and outdoor environments. The data capture system may further be configured to acquire seamless texture data from a 360° horizontal and vertical perspective to support panoramic video and images.

Abstract: Examples of the present disclosure describe systems and methods for capturing data to acquire indoor and outdoor geometry. In aspects, a data capture system may be configured to acquire texture data, geometry data, navigation data and/or orientation data to support geolocation and georeferencing within indoor and outdoor environments. The data capture system may further be configured to acquire seamless texture data from a 360° horizontal and vertical perspective to support panoramic video and images.

Abstract: Mobile platforms are used to capture an area using a variety of sensors (e.g., cameras and laser scanners) while traveling through the area, in order to create a representation (e.g., a navigable set of panoramic images, or a three-dimensional reconstruction). However, such sensors are often precisely calibrated in a controlled setting, and miscalibration during travel (e.g., due to a physical jolt) may result in a corruption of data and/or a recalibration that leaves the platform out of service for an extended duration. Presented herein are techniques for verifying sensor calibration during travel. Such techniques involve the identification of a sensor path for each sensor over time (e.g., a laser scanner path, a camera path, and a location sensor path) and a comparison of the paths, optionally after registration with a static coordinate system, to verify that the continued calibration of the sensors during the mobile operation of the platform.

Abstract: Examples of the present disclosure describe systems and methods for capturing data to acquire indoor and outdoor geometry. In aspects, a data capture system may be configured to acquire texture data, geometry data, navigation data and/or orientation data to support geolocation and georeferencing within indoor and outdoor environments. The data capture system may further be configured to acquire seamless texture data from a 360° horizontal and vertical perspective to support panoramic video and images.

Abstract: Mobile platforms are used to capture an area using a variety of sensors (e.g., cameras and laser scanners) while traveling through the area, in order to create a representation (e.g., a navigable set of panoramic images, or a three-dimensional reconstruction). However, such sensors are often precisely calibrated in a controlled setting, and miscalibration during travel (e.g., due to a physical jolt) may result in a corruption of data and/or a recalibration that leaves the platform out of service for an extended duration. Presented herein are techniques for verifying sensor calibration during travel. Such techniques involve the identification of a sensor path for each sensor over time (e.g., a laser scanner path, a camera path, and a location sensor path) and a comparison of the paths, optionally after registration with a static coordinate system, to verify that the continued calibration of the sensors during the mobile operation of the platform.

Abstract: Mobile platforms are used to capture an area using a variety of sensors (e.g., cameras and laser scanners) while traveling through the area, in order to create a representation (e.g., a navigable set of panoramic images, or a three-dimensional reconstruction). However, such sensors are often precisely calibrated in a controlled setting, and miscalibration during travel (e.g., due to a physical jolt) may result in a corruption of data and/or a recalibration that leaves the platform out of service for an extended duration. Presented herein are techniques for verifying sensor calibration during travel. Such techniques involve the identification of a sensor path for each sensor over time (e.g., a laser scanner path, a camera path, and a location sensor path) and a comparison of the paths, optionally after registration with a static coordinate system, to verify that the continued calibration of the sensors during the mobile operation of the platform.

Abstract: Mobile platforms are used to capture an area using a variety of sensors (e.g., cameras and laser scanners) while traveling through the area, in order to create a representation (e.g., a navigable set of panoramic images, or a three-dimensional reconstruction). However, such sensors are often precisely calibrated in a controlled setting, and miscalibration during travel (e.g., due to a physical jolt) may result in a corruption of data and/or a recalibration that leaves the platform out of service for an extended duration. Presented herein are techniques for verifying sensor calibration during travel. Such techniques involve the identification of a sensor path for each sensor over time (e.g., a laser scanner path, a camera path, and a location sensor path) and a comparison of the paths, optionally after registration with a static coordinate system, to verify that the continued calibration of the sensors during the mobile operation of the platform.