Abstract: A system and method for generating an importance occupancy grid map (OGM) for a vehicle are disclosed. The method includes: receiving a three-dimensional (3D) point cloud; receiving a binary map, the binary map associated with a set of GPS coordinates of the vehicle; receiving information representative of a planned path for the vehicle; and generating an importance OGM based on the 3D point cloud, the binary map, and the planned path for the vehicle using a map generation module.

Abstract: A system and method for generating a bounding box for an object in proximity to a vehicle are disclosed. The method includes: receiving a three-dimensional (3D) point cloud representative of an environment; receiving a two-dimensional (2D) image of the environment; processing the 3D point cloud to identify an object cluster of 3D data points for a 3D object in the 3D point cloud; processing the 2D image to detect a 2D object in the 2D image and generate information regarding the 2D object from the 2D image; and when the 3D object and the 2D object correspond to the same object in the environment: generating a bird's eye view (BEV) bounding box for the object based on the object cluster of 3D data points and the information from the 2D image.

Abstract: A system and method for generating a bounding box for an object in proximity to a vehicle are disclosed. The method includes: receiving a three-dimensional (3D) point cloud representative of an environment; receiving a two-dimensional (2D) image of the environment; processing the 3D point cloud to identify an object cluster of 3D data points for a 3D object in the 3D point cloud; processing the 2D image to detect a 2D object in the 2D image and generate information regarding the 2D object from the 2D image; and when the 3D object and the 2D object correspond to the same object in the environment: generating a bird's eye view (BEV) bounding box for the object based on the object cluster of 3D data points and the information from the 2D image.

Abstract: A system and method for generating an importance occupancy grid map (OGM) for a vehicle are disclosed. The method includes: receiving a three-dimensional (3D) point cloud; receiving a binary map, the binary map associated with a set of GPS coordinates of the vehicle; receiving information representative of a planned path for the vehicle; and generating an importance OGM based on the 3D point cloud, the binary map, and the planned path for the vehicle using a map generation module.

Abstract: System and method of partitioning a plurality of data objects that are each represented by a respective high dimensional feature vector is described, including performing a hashing function on each high dimensional feature vector to generate a respective lower dimensional binary compact feature vector for the data object that is represented by the high dimensional feature vector; performing a further hashing function on each compact feature vector to assign a sub-index ID to the compact feature vector; and partitioning the compact feature vectors into respective partition groups that correspond to the sub-index IDs assigned to the compact feature vectors.

Abstract: Methods and apparatuses for generating a frame of semantically labeled 2D data are described. A frame of sparse 3D data is generated from a frame of sparse 3D data. Semantic labels are assigned to the frame of dense 3D data, based on a set of 3D bounding boxes determined for the frame of sparse 3D data. Semantic labels are assigned to a corresponding frame of 2D data based on a mapping between the frame of sparse 3D data and the frame of 2D data. The mapping is used to map a 3D data point in the frame of dense 3D data to a mapped 2D data point in the frame of 2D data. The semantic label assigned to the 3D data point is assigned to the mapped 2D data point. The frame of semantically labeled 2D data, including the assigned semantic labels, is outputted.

Abstract: System and method of generating an index structure for indexing a plurality of unstructured data objects, including: generating a set of compact feature vectors, the set including a compact feature vector for each of the data objects, the compact feature vector for each data object including a sequence of hashed values that represent the data object; generating a plurality of twisted compact feature vector sets for each of set of compact feature vectors, each of the twisted compact feature vector sets being generated by applying a respective random shuffling permutation to the set of compact feature vectors; and for each twisted compact feature vector set, generating an index for the data objects in which the data objects are slotted based on sequences of hashed values in the twisted compact feature vector set.

Abstract: A system and method for performing camera-LIDAR calibration based on a checkerboard placed in proximity to a vehicle, the method includes: receiving a 3D point cloud and a 2D image including the checkerboard; filtering the 3D point cloud representing the checkerboard; converting the filtered 3D point cloud to a 2D point cloud in a translated coordinate system; estimating a 2D position, in the translated coordinate system, for each outer corner of the checkerboard represented by the 2D point cloud; estimating a 2D position in the translated coordinate system for each inner corner of the checkerboard represented by the 2D point cloud; determining a 3D position, in a LIDAR coordinate system, for each corner of the checkerboard in the 3D point cloud based on the corresponding 2D position in the translated coordinate system; and determining a 2D position of each corner of the checkerboard in a 2D image coordinate system.

Abstract: Methods and apparatuses for generating a frame of semantically labeled 2D data are described. A frame of sparse 3D data is generated from a frame of sparse 3D data. Semantic labels are assigned to the frame of dense 3D data, based on a set of 3D bounding boxes determined for the frame of sparse 3D data. Semantic labels are assigned to a corresponding frame of 2D data based on a mapping between the frame of sparse 3D data and the frame of 2D data. The mapping is used to map a 3D data point in the frame of dense 3D data to a mapped 2D data point in the frame of 2D data. The semantic label assigned to the 3D data point is assigned to the mapped 2D data point. The frame of semantically labeled 2D data, including the assigned semantic labels, is outputted.

Abstract: System and method of partitioning a plurality of data objects that are each represented by a respective high dimensional feature vector is described, including performing a hashing function on each high dimensional feature vector to generate a respective lower dimensional binary compact feature vector for the data object that is represented by the high dimensional feature vector; performing a further hashing function on each compact feature vector to assign a sub-index ID to the compact feature vector; and partitioning the compact feature vectors into respective partition groups that correspond to the sub-index IDs assigned to the compact feature vectors.

Abstract: System and method of generating an index structure for indexing a plurality of unstructured data objects, including: generating a set of compact feature vectors, the set including a compact feature vector for each of the data objects, the compact feature vector for each data object including a sequence of hashed values that represent the data object; generating a plurality of twisted compact feature vector sets for each of set of compact feature vectors, each of the twisted compact feature vector sets being generated by applying a respective random shuffling permutation to the set of compact feature vectors; and for each twisted compact feature vector set, generating an index for the data objects in which the data objects are slotted based on sequences of hashed values in the twisted compact feature vector set.