Resolving Place Recognition Inconsistencies Using Intra-Set Similarities

Abstract

Place recognition is the problem of finding associations between a query set of place descriptions and a database. It is an important means for loop closure detection in SLAM. The primary source of information to decide about associations is the pairwise similarity of descriptors between the query and the database items (e.g., image descriptor similarities). Beyond better descriptors, significant improvements were achieved by exploiting additional structural information, in particular by comparing sequences instead of individual items. In this letter, we propose to use another systematic source of information: intra-set similarities between items within the query or the database sets. They can be used to detect inconsistencies of groups of associations between database and query items, e.g. to inhibit matchings of multiple query descriptors to the same database descriptor if the query descriptors are mutually different. The underlying idea is a heuristic tightening of the triangle inequality of groups of descriptors. Based on a definition of matching inconsistencies, we propose an Inconsistency Resolution Procedure (IRP) to modify the inter-set similarities between database and query in a way that resolves existing inconsistencies with intra-set similarities. Our experiments show an average place recognition performance gain of >30% in a general place recognition setup with 21 datasets and two state of the art image processing front-ends. The proposed approach does not require additional information beyond descriptor similarities, makes no assumptions of sequences, does not require training, and has no parameter that needs adjustment. It can be combined with other established techniques like descriptor standardization and sequence processing.