This invention belongs to the field of
lidar odometry technology and provides a
lidar odometry optimization method and
system based on resource occupancy cost. Based on raw
point cloud data and preset geometric feature algorithms, general feature algorithms, and direct registration algorithms, corresponding candidate poses and
feature matching errors are obtained respectively. Based on CPU utilization, memory utilization,
cache hit rate, and a preset resource occupancy cost model, the total resource occupancy cost is obtained. Based on the candidate
pose,
feature matching error, and total resource occupancy cost, the optimal
pose estimation is determined with the minimum total cost as the optimization objective. By introducing the resource occupancy cost model,
resource efficiency is taken as one of the core optimization objectives, enabling the
odometry to dynamically adjust the
algorithm strategy according to the current
system load. This pursues high accuracy when resources are abundant and ensures smooth
system operation when resources are limited, effectively avoiding positioning failures caused by computational overload.