The rigid requirement of traditional multi-sensor fusion technology for precise spatiotemporal alignment of sensors and pixel-level accurate annotation leads to high engineering implementation costs, complex calibration processes, insufficient system robustness, and long annotation cycles. This paper proposes an end-to-end front fusion implementation scheme for multi-sensors based on deviation tolerance, abandoning the requirements of high-precision annotation and strong spatiotemporal alignment, constructing a global imprecise unified annotation system that allows errors between sensor data and annotation results within a reasonable deviation range; designing a front fusion process with coarse alignment of multi-source data to simplify engineering deployment and data preprocessing links; building a real-time deviation monitoring and over-limit alarm mechanism that automatically triggers maintenance prompts when the deviation exceeds the preset tolerance threshold. This paper elaborates on the imprecise annotation specifications, data fusion implementation process, deviation monitoring logic and engineering adaptation scheme in detail, and presents the specific construction method and training strategy of the multi-channel input network. Experimental verification shows that this scheme can greatly reduce the cost of annotation and calibration, shorten the implementation cycle, and ensure the fusion perception accuracy, providing a directly reusable technical scheme for the mass production and implementation of multi-sensor fusion technology in fields such as autonomous driving and intelligent robots.
Wei Yang (Mon,) studied this question.