Multiple Resolution, Simultaneous Localization And Mapping Based On 3-D Lidar Measurements

Methods and systems for improved simultaneous localization and mapping based on 3-D LIDAR image data are presented herein. In one aspect, LIDAR image frames are segmented and clustered before feature detection to improve computational efficiency while maintaining both mapping and localization accuracy. Segmentation involves removing redundant data before feature extraction. Clustering involves grouping pixels associated with similar objects together before feature extraction. In another aspect, features are extracted from LIDAR image frames based on a measured optical property associated with each measured point. The pools of feature points comprise a low resolution feature map associated with each image frame.Low resolution feature maps are aggregated over time to generate high resolution feature maps. In another aspect, the location of a LIDAR measurement system in a three dimensional environment is slowly updated based on the high resolution feature maps and quickly updated based on the low resolution feature maps.