Improving Condition- and Environment-Invariant Place Recognition with Semantic Place Categorization

TL;DR

This paper introduces a hybrid place recognition system that leverages semantic place categorization to improve robustness against environmental changes, outperforming existing methods across multiple datasets.

Contribution

The paper presents a novel hybrid approach combining place recognition and categorization, significantly enhancing robustness to environmental variations.

Findings

Semantic categorization segments space effectively

Improved place recognition accuracy in diverse conditions

Outperforms state-of-the-art systems on benchmark datasets

Abstract

The place recognition problem comprises two distinct subproblems; recognizing a specific location in the world ("specific" or "ordinary" place recognition) and recognizing the type of place (place categorization). Both are important competencies for mobile robots and have each received significant attention in the robotics and computer vision community, but usually as separate areas of investigation. In this paper, we leverage the powerful complementary nature of place recognition and place categorization processes to create a new hybrid place recognition system that uses place context to inform place recognition. We show that semantic place categorization creates an informative natural segmenting of physical space that in turn enables significantly better place recognition performance in comparison to existing techniques. In particular, this new semantically informed approach adds robustness to significant local changes within the environment, such as transitioning between indoor and outdoor environments or between dark and light rooms in a house, complementing the capabilities of current condition-invariant techniques that are robust to globally consistent change (such as day to night cycles). We perform experiments using 4 benchmark and new datasets and show that semantically-informed place recognition outperforms the previous state-of-the-art systems. Like it does for object recognition [1], we believe that semantics can play a key role in boosting conventional place recognition and navigation performance for robotic systems.

Full text

See pages 1-last of iros-2017.pdf