前融合策略下辅助智能驾驶的多模态数据采集与高精度点云地图构建

Multimodal data collection and high-precision point cloud map construction for assisted intelligent driving under the pre-fusion strategy

随着人工智能和泛在传感技术的迅猛发展,以多模态数据融合处理为基础的智能辅助驾驶系统逐渐走入千家万户。智能驾驶技术的核心是依赖于先进的人工智能和泛在传感器技术,以增强或替代驾驶人的感知、决策和执行能力。其中,对道路环境实时、精准、稳健的感知是汽车智能化的重要一环。本文介绍了一种集成多源传感器的多模态数据采集车辆平台,通过构建高精度点云地图,为辅助智能驾驶提供基础点云数据服务。不同于“后融合”的多源数据融合策略,本文采取经时空同步多源数据进行“前融合”的策略,在完成多源传感器数据同步与校准的基础上,为智能驾驶车辆提供时间空间一致的感知数据。在地图构建层面上,通过与IMU和固态激光雷达的耦合,实现了对环境点云地图的高精度重建,为辅助智能驾驶的实现和进一步发展提供了重要技术支持。

With the rapid development of artificial intelligence and ubiquitous sensing technology,intelligent assisted driving systems based on multimodal data fusion processing are gradually entering households.The core of intelligent driving technology relies on advanced artificial intelligence and ubiquitous sensor technology to enhance or replace the perception,decision-making,and execution capabilities of drivers.Among them,real-time,accurate,and robust perception of the road environment is an important part of vehicle intelligence.This article introduces a vehicle platform that integrates multiple sensors for multimodal data collection,and provides basic point cloud data services for assisted intelligent driving by constructing high-precision point cloud maps.Different from the“post-fusion”strategy of multi-source data fusion,this article adopts the strategy of“pre-fusion”with time-space synchronized multi-source data.Based on completing the synchronization and calibration of multi-source sensor data,it provides intelligent driving vehicles with perception data that is consistent in time and space.At the level of map construction,this article achieves high-precision reconstruction of environmental point cloud maps by coupling with IMU and solid-state lidar,providing important technical support for the realization and further development of assisted intelligent driving.