BACKGROUND

An autonomous vehicle is a motorized vehicle that can operate without a human driver. An autonomous vehicle can include, for example, multiple sensor systems, such as a lidar sensor system, an imaging sensor system, and a radar sensor system, amongst others. The autonomous vehicle utilizes sensor signals output by the sensor systems to control its operation.

As the autonomous vehicle is operated, sensor signals from a sensor system can provide a substantive amount of data representative of the surroundings of the autonomous vehicle. Such data can include point cloud data representative of a region relative to the autonomous vehicle and, thus, can have a rich, complex structure. Further, the data can be formed from input signals from several sensor systems of the autonomous vehicle. Such a consolidation can render the data even more voluminous. According to an illustration, each point in three-dimensional (3D) space maintained in a point cloud data structure can have multiple channels (signals) such as, for example, position of a point in 3D space, point cloud distribution around the point (covariance), surface normal vector around the point, color channels, etc.

As such, storage of point cloud data typically includes compression of at least some of the point cloud data. Yet, conventional technologies to compress point cloud data typically rely on decompression of compressed point cloud data prior to operating mathematically on such data. Even as memory devices become smaller and cheaper, issues including memory fragmentation, virtualization, and other memory management techniques can preclude from enlarging the data storage available in an autonomous vehicle. Therefore, much remains to be improved in conventional technologies to compress and manipulate data representative of the surroundings of an autonomous vehicle.

SUMMARY

The following is a brief summary of subject matter that is described in greater detail herein. This summary is not intended to be limiting as to the scope of the claims.

The disclosure recognizes and addresses, in at least some embodiments, the issue of compression of point cloud data and other processing of such data in autonomous vehicles. More specifically, yet not exclusively, the disclosure provides autonomous vehicles and techniques to compress point cloud data and perform mathematical operations on compressed point cloud data.

In some embodiments, an autonomous vehicle can include a sensor system that generates point cloud data representative of a region relative to the autonomous vehicle. The sensor system can supply at least a portion of the point cloud data to a data compression system of the autonomous vehicle. The data compression system can configure the received point cloud data according to a collection of three-dimensional (3D) tiles representative of the region. Splitting the 3D space into 3D tiles can enable limiting a numerical range to represent each point within the point cloud when expressed with respect to the tile it belongs. Each 3D tile of the 3D tiles can include a portion of the cloud point data, where each point vector in the portion of the cloud point data can be configured relative to a position vector of the 3D tile defined in a coordinate system of the collection of 3D tiles. It is contemplated that the point cloud aggregation and compression described herein can be performed on-the-fly, for example. According to another example, the data compression system can be utilized for an offline procedure such that the point cloud can be created as an aggregate of sensor data snapshots, which can then be compressed together to enable creating point cloud maps.

The data compression system can utilize a fixed-point Q-format representation based on a defined number of bits in order to determine an integer representation of the point vector. The integer representation permits replacing the point coordinates of the point vector, initially in floating point representation, with respective rational numbers based at least on the defined number of bits and a size of the 3D tile. For a collection of 3D tiles having a same size for each tile, the data compression system can retain integer numbers for respective point vector coordinates and a point vector representing the origin of each tile (which can additionally or alternatively be inferred from the index and the size of the tile), thus compressing the received point cloud data.

The autonomous vehicle also can include a control system that can operate mathematically on compressed point cloud data, without reliance on prior decompression of the point cloud data operated upon. The control system can guide motion of the autonomous vehicle based at least on results from operating on the compressed point cloud data. Moreover, it is contemplated that a map-based localization system (e.g., sensor point cloud alignment against the point cloud map) or a perception system (e.g., match traffic lights, signs seen by the sensors against the point cloud map) of the autonomous vehicle can utilize the compressed point cloud data.

Although embodiments of the disclosure are illustrated herein with reference to point cloud data, the disclosure is not limited in that respect and the principles of the disclosure can be implemented for other types of data including numeric data records.

Embodiments of the disclosure provide several technical benefits. For example, an integer representation of point cloud data of this disclosure reduces bandwidth usage for storage and retrieval of compressed point cloud data. In addition, greater speed of storage of compressed point cloud data and greater speed of access of stored compressed point cloud data can be achieved by processing integer numbers according to a defined number of bits. As another example, not only can the compressed data be operated upon directly, without prior decompression, but the processing speed to operate mathematically on the compressed data increases because integer arithmetic logic units (ALUs) and graphics processing units (GPUs) can be utilized for processing the compressed point cloud data. Further, by relying on a fixed-point Q-format representation, fixed precision can permit or otherwise facilitate determining numerical error bounds and/or performing significant digit analysis. At least the foregoing technical benefits can provide superior reliance, flexibility, and/or efficiency relative to conventional technologies for compression of data and manipulation of compressed data. These and other technical benefits can be realized through an implementation of the disclosed technologies.

The above summary presents a simplified summary in order to provide a basic understanding of some aspects of the systems and/or methods discussed herein. This summary is not an extensive overview of the systems and/or methods discussed herein. It is not intended to identify key/critical elements or to delineate the scope of such systems and/or methods. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.

