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 labels for potentially non-impeding objects in an environment are disclosed. Unlabeled lidar segments may be evaluated to determine whether they are located in a drivable road in an environment and to determine a lidar intensity value for the segments. Based on the intensity and whether the segment is in a drivable region, the system determines a corresponding range of lidar values associated with a label or no label. The system assigns the label (or no label) associated with the corresponding range. The label and associated segment data may then be used to classify an object associated with the segment, control a vehicle, and train a machine-learned model.

Abstract: Techniques for determining labels and occupancy data for voxels and pixels representing object protrusions are disclosed. The occupancy status of voxels surrounding an occupied voxel is determined and used to determine the occupancy density of the occupied voxel. The loss for the occupied voxel is adjusted inversely proportionately to the occupancy density. The adjusted-loss voxel is used to train a machine-learned model to detect objects in an environment and, specifically, to more accurately detect objects having protrusions that may otherwise not be associated with the object. This model may be used to provide data used to control a vehicle.

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 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 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 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: 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 estimating ground height based on lidar data are discussed herein. A vehicle captures 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.