Probabilistic Data Association for Semantic SLAM at Scale

TL;DR

This paper introduces a probabilistic data association method for semantic SLAM that improves landmark measurement assignment in repetitive environments, enabling real-time, scalable, and more accurate long-term mapping using semantic information.

Contribution

It proposes a novel real-time probabilistic data association framework for semantic SLAM that enhances landmark recognition and map sparsity in repetitive environments.

Findings

Effective in real-time on KITTI dataset

Improves landmark assignment accuracy

Enables scalable long-term SLAM

Abstract

With advances in image processing and machine learning, it is now feasible to incorporate semantic information into the problem of simultaneous localisation and mapping (SLAM). Previously, SLAM was carried out using lower level geometric features (points, lines, and planes) which are often view-point dependent and error prone in visually repetitive environments. Semantic information can improve the ability to recognise previously visited locations, as well as maintain sparser maps for long term SLAM applications. However, SLAM in repetitive environments has the critical problem of assigning measurements to the landmarks which generated them. In this paper, we use k-best assignment enumeration to compute marginal assignment probabilities for each measurement landmark pair, in real time. We present numerical studies on the KITTI dataset to demonstrate the effectiveness and speed of the proposed framework.