Detecting Salient Places for SLAM Urban Environments Using Multispectral Data

Real-time SLAM in large-scale environments is a computationally demanding task. This paper discusses an approach to dealing with storage and computation constraints based on selective evidence gathering at significant areas in the environment. Local measures of novelty, based on the input from multiple sensors, are used to detect salient locations. This can then be used to trigger the collection of reliable descriptors of the location. By biasing evidence collection toward these significant areas, we hope to achieve a large reduction in processing and storage requirements without significant impact on SLAM performance.