EMPNet: Neural Localisation and Mapping Using Embedded Memory Points

TL;DR

EMPNet introduces a neural memory module with aligned point-embeddings for improved localization and mapping, integrating scene structure from RGB-D data to enhance robustness and accuracy in autonomous systems.

Contribution

The paper presents a novel end-to-end trainable memory module with rigidly aligned point-embeddings for neural localization and mapping.

Findings

Significant performance improvements on VIZDoom environment.

Enhanced robustness and accuracy in real-world Active Vision Dataset.

Effective integration of scene structure from RGB-D sequences.

Abstract

Continuously estimating an agent's state space and a representation of its surroundings has proven vital towards full autonomy. A shared common ground among systems which successfully achieve this feat is the integration of previously encountered observations into the current state being estimated. This necessitates the use of a memory module for incorporating previously visited states whilst simultaneously offering an internal representation of the observed environment. In this work we develop a memory module which contains rigidly aligned point-embeddings that represent a coherent scene structure acquired from an RGB-D sequence of observations. The point-embeddings are extracted using modern convolutional neural network architectures, and alignment is performed by computing a dense correspondence matrix between a new observation and the current embeddings residing in the memory module. The whole framework is end-to-end trainable, resulting in a recurrent joint optimisation of the point-embeddings contained in the memory. This process amplifies the shared information across states, providing increased robustness and accuracy. We show significant improvement of our method across a set of experiments performed on the synthetic VIZDoom environment and a real world Active Vision Dataset.