Unmanned multi-laser radar collaborative simulation method based on GPU acceleration

Abstract

The invention discloses an unmanned multi-laser radar collaborative simulation method based on GPU acceleration, and the method is characterized in that the method comprises the following steps: 1, obtaining the depth information of laser radars at the initial position of a vehicle, converting the depth information into point cloud output corresponding to the laser radars, and obtaining the depth information and point cloud of the laser radars at the initial position; step 2, based on the latest position of the vehicle, using a GPU to generate a depth map surrounding the vehicle; step 3, constructing an optimization loss function and an optimization problem based on the depth map; step 4, simplifying and optimizing a loss function; 5, solving the optimal solution of the optimization problem; 6, judging whether the optimization loss function reaches an optimization threshold value or not according to the optimal solution; and 7, converting the depth information of the laser radar into a corresponding point cloud.

Description

technical field [0001] The invention relates to the field of unmanned driving, in particular to a GPU-accelerated multi-lidar collaborative simulation method for unmanned driving. Background technique [0002] Since lidar is widely used in unmanned driving and used to generate point clouds, its simulation has also received more attention and challenges. Multi-line lidar needs to emit a large number of laser beams at high frequency, so the simulation requires real-time and high-performance calculations, especially in real simulation requirements, it is often necessary to carry out co-simulation on a single vehicle equipped with multiple lidars, which further increases the requirements for computing resources. Big. [0003] However, the current unmanned driving simulation software is inefficient in the simulation of lidar. For example, well-known simulation platforms such as CARLA use a CPU-based method for lidar to calculate the intersection of laser and object to generate p...

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Problems solved by technology

[0003] However, the current unmanned driving simulation software is inefficient in the simulation of lidar. For example, well-known simulation platforms such as CARLA use a CPU-based method for lidar to calculate the intersection of laser and object to generate point clouds. The low efficiency is far from meeting the simulation needs. Even if the simulation platform SVLSimulator developed by LGSiliconValleyLab adopts the method of GPU acceleration, it is difficult to efficiently co-simulate multiple lidars under resource constraints.

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