Abstract: Techniques for determining labels for potentially small, moving, and non-impeding objects in an environment are disclosed. Unlabeled lidar segments may be evaluated to determine whether segments are associated with objects located in a drivable region and to determine one or more characteristics of such segments. This determination includes comparing segment characteristics to various criteria that may be associated with small, dynamic objects, such as object size, motion, occlusion, and/or solidity. Based on this evaluation and whether the segment is in a drivable region, the system determines whether to label the segment as a small, dynamic, non-impeding object.

Abstract: Techniques for determining a probability that an object that has been appropriately labeled as a non-impeding object are disclosed. Non-impeding objects may be objects in an environment that do not substantially affect the operation of a vehicle in the environment, such as steam, fog, smoke, etc. A machine-learned model may classify such objects as non-impeding objects. The designation may be checked by using the lidar data and measurements associated with the lidar points associated with the object to determine a probability that the object is a non-impeding object. The lidar data may be aggregated within the individual voxels of a voxelized representation of the object for use in the probability determination.

Abstract: Techniques for detecting and classifying objects using lidar data are discussed herein. In some cases, the system may be configured to utilize a predetermined number of prior frames of lidar data to assist with detecting and classifying objects. In some implementations, the system may utilize a subset of the data associated with the prior lidar frames together with the full set of data associated with a current frame to detect and classify the objects.

Abstract: Techniques for identifying lidar points associated with static objects, and using such lidar points to annotate objects within two-dimensional images are discussed herein. In some examples, an object manager may receive accumulations of lidar data captured from lidar devices of a vehicle while traversing within a driving environment. In some examples, the object manager may receive a plurality of annotated images. Such annotations may identify static objects within the driving environment. In some instances, the object manager may project a lidar point into an annotated image and determine that the lidar point is associated with an annotated pixel. Based on the pixel being associated with the annotated object, the object manager may determine that the lidar point is associated with object. In some examples, the object manager may determine a subset of lidar points that are associated with the object.

Abstract: Techniques for densifying annotations associated with sensor data are discussed herein. For example, techniques may include projecting a subset of lidar points into an unannotated two-dimensional image. Based on the projecting, the annotations of lidar points projected into the image can be associated with the image data. A subset of pixels of pixels can be dilated, and the dilated pixels can be determined to intersect (e.g., overlap, touch, etc.). Based on the pixels intersecting (or being adjacent) and being associated with the same segment identifier, a contour can be rendered around the corresponding non-dilated pixels to identify an object in the image data.

Abstract: Techniques for identifying lidar points associated with static objects, and using such lidar points to annotate objects within two-dimensional images are discussed herein. In some examples, an object manager may receive accumulations of lidar data captured from lidar devices of a vehicle while traversing within a driving environment. In some examples, the object manager may receive a plurality of annotated images. Such annotations may identify static objects within the driving environment. In some instances, the object manager may project a lidar point into an annotated image and determine that the lidar point is associated with an annotated pixel. Based on the pixel being associated with the annotated object, the object manager may determine that the lidar point is associated with object. In some examples, the object manager may determine a subset of lidar points that are associated with the object.

Abstract: Techniques for detecting and classifying objects using lidar data are discussed herein. In some cases, the system may be configured to utilize a predetermined number of prior frames of lidar data to assist with detecting and classifying objects. In some implementations, the system may utilize a subset of the data associated with the prior lidar frames together with the full set of data associated with a current frame to detect and classify the objects.

Abstract: A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.

Abstract: The invention relates to a method for operating a grab system, comprising a carrier device and a cable grab arranged thereon, which cable grab comprises a grab frame, which is held by a retaining cable on the carrier device, at least two grab buckets, which are mounted on a lower end of the grab frame, so as to be able to swivel between a closed position and an open position, and an actuating device with an actuating cable for swivelling the grab buckets, wherein an actuating cable is guided from the carrier device to the actuating device, and the carrier device has a driven first cable winch for the retaining cable and a driven second cable winch for the actuating cable, wherein the carrier device has an undercarriage and an upper carriage, which is mounted thereon for rotation about a vertical axis and has a cantilever arm on which the cable grab is vertically adjustably mounted, wherein in a removal step, in order to form a trench in the ground, the cable grab is lowered into the ground by the retaining ca

Abstract: A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.

Abstract: The invention relates to a civil engineering device, in particular cable grab, with a support apparatus, a processing unit, which is configured to carry out a civil engineering step and is held by way of at least one cable on the support apparatus, at least one cable winch for operating the cable, wherein the cable winch has a winch drum and an associated winch motor for driving the winch drum rotationally, at least one free fall brake, which is arranged on the at least one cable winch, wherein in a closed state of the free fall brake, the winch drum is coupled to the associated winch motor, in a partially open state of the free fall brake, a relative movement is possible between winch drum and winch motor, wherein a torque that is dependent on the degree of opening of the free fall brake is transmitted in the free fall brake, and in an open state the winch drum is decoupled from the associated winch motor and is substantially freely rotatable with respect to this.

Abstract: The invention relates to a grab device with a carrier implement and a rope grab arranged thereon which has a grab frame which is held on the carrier implement via a holding rope, at least two grab shovels which are pivotably supported on a lower end of the grab frame between a closed position and an open position, and an actuating means with an actuating rope for pivoting the grab shovels, wherein an actuating rope is guided from the carrier implement to the actuating means and the carrier implement has a driven first rope winch for the holding rope and a driven second rope winch for the actuating rope.

Abstract: Techniques for identifying curbs are discussed herein. For instance, a vehicle may generate sensor data using one or more sensors, where the sensor data represents points associated with a driving surface and a sidewalk. The vehicle may then quantize the points into distance bins that are located laterally along the driving direction of the vehicle in order to generate spatial lines. Next, the vehicle may determine separation points for the spatial lines, where the separation points are configured to separate the points associated with the driving surface from the points associated with the sidewalk. The vehicle may then generate, using the separation points, a curve that represents the curb between the driving surface and the sidewalk. This way, the vehicle may use the curve while navigating, such as to avoid the curb and/or stop at a location that is proximate to the curb.

Abstract: A system may include one or more processors configured to receive a plurality of images representing an environment. The images may include image data generated by an image capture device. The processors may also be configured to transmit the image data to an image segmentation network configured to segment the images. The processors may also be configured to receive sensor data associated with the environment including sensor data generated by a sensor of a type different than an image capture device. The processors may be configured to associate the sensor data with segmented images to create a training dataset. The processors may be configured to transmit the training dataset to a machine learning network configured to run a sensor data segmentation model, and train the sensor data segmentation model using the training dataset, such that the sensor data segmentation model is configured to segment sensor data.

Abstract: A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.

Abstract: Techniques for detecting an object in an environment and determining a probability that the object is a cloud of particulate matter. The cloud of particulate matter may include steam (e.g., emitted from a man-hole cover, a dryer exhaust port, etc.), exhaust from a vehicle (e.g., car, truck, motorcycle, etc.), environmental gases (e.g., resulting from sublimation, fog, evaporation, etc.), a cloud of dust, water splashing, blowing leaves, or other types of particulate matter that may be located in the environment of the vehicle and may not impact driving behavior (e.g., an autonomous vehicle may safely pass through the particulate matter without impact to the platform). A vehicle computing system may determine the probability that the object is a cloud of particulate matter and may control the vehicle based on the probability.

Abstract: Techniques for controlling a vehicle based on height data and/or classification data being determined utilizing multi-channel image data are discussed herein. The vehicle can capture lidar data as it traverses an environment. The lidar data can be associated with a voxel space as three-dimensional data. Semantic information can be determined and associated with the lidar data and/or the three-dimensional voxel space. A multi-channel input image can be determined based on the three-dimensional voxel space and input into a machine learned (ML) model. The ML model can output data to determine height data and/or classification data associated with a ground surface of the environment. The height data and/or classification data can be utilized to determine a mesh associated with the ground surface. The mesh can be used to control the vehicle and/or determine additional objects proximate the vehicle.

Abstract: A vehicle can include various sensors to detect objects in an environment. Sensor data can be captured by a perception system in a vehicle and represented in a voxel space. Operations may include analyzing the data from a top-down perspective. From this perspective, techniques can associate and generate masks that represent objects in the voxel space. Through manipulation of the regions of the masks, the sensor data and/or voxels associated with the masks can be clustered or otherwise grouped to segment data associated with the objects.

Abstract: Techniques for detecting and classifying objects using lidar data are discussed herein. In some cases, the system may be configured to utilize a predetermined number of prior frames of lidar data to assist with detecting and classifying objects. In some implementations, the system may utilize a subset of the data associated with the prior lidar frames together with the full set of data associated with a current frame to detect and classify the objects.

Abstract: A system may include one or more processors configured to receive a plurality of images representing an environment. The images may include image data generated by an image capture device. The processors may also be configured to transmit the image data to an image segmentation network configured to segment the images. The processors may also be configured to receive sensor data associated with the environment including sensor data generated by a sensor of a type different than an image capture device. The processors may be configured to associate the sensor data with segmented images to create a training dataset. The processors may be configured to transmit the training dataset to a machine learning network configured to run a sensor data segmentation model, and train the sensor data segmentation model using the training dataset, such that the sensor data segmentation model is configured to segment sensor data.