Incremental Abstraction in Distributed Probabilistic SLAM Graphs (2109.06241v2)

Abstract: Scene graphs represent the key components of a scene in a compact and semantically rich way, but are difficult to build during incremental SLAM operation because of the challenges of robustly identifying abstract scene elements and optimising continually changing, complex graphs. We present a distributed, graph-based SLAM framework for incrementally building scene graphs based on two novel components. First, we propose an incremental abstraction framework in which a neural network proposes abstract scene elements that are incorporated into the factor graph of a feature-based monocular SLAM system. Scene elements are confirmed or rejected through optimisation and incrementally replace the points yielding a more dense, semantic and compact representation. Second, enabled by our novel routing procedure, we use Gaussian Belief Propagation (GBP) for distributed inference on a graph processor. The time per iteration of GBP is structure-agnostic and we demonstrate the speed advantages over direct methods for inference of heterogeneous factor graphs. We run our system on real indoor datasets using planar abstractions and recover the major planes with significant compression.

• The paper introduces an incremental abstraction framework that fuses neural network outputs with probabilistic inference to build compact, semantically rich SLAM graphs.
• It employs distributed Gaussian Belief Propagation for real-time inference on dynamic factor graphs, enabling efficient map updates without increased computational cost.
• Experimental results show faster convergence and improved tracking accuracy, particularly in planar environments as validated against the TUM dataset.

Incremental Abstraction in Distributed Probabilistic SLAM Graphs

The paper "Incremental Abstraction in Distributed Probabilistic SLAM Graphs" addresses the complex challenge of constructing semantically rich and compact scene graphs as part of the Simultaneous Localization and Mapping (SLAM) process. The authors introduce a novel approach integrating incremental abstraction and distributed probabilistic inference to enhance graph-based SLAM systems, particularly in the context of monocular SLAM.

Core Contributions

The authors detail two primary contributions:

1. Incremental Abstraction Framework: This component leverages a combination of an off-the-shelf neural network and probabilistic inference to propose and confirm abstract scene elements. This approach allows for the dynamic integration of abstract entities like planes into the SLAM factor graph. The framework seeks to replace raw positional data with more semantically dense and compact representations, leading to efficient scene comprehension and representation.
2. Distributed Optimization Using Gaussian Belief Propagation (GBP): The paper pioneers the use of GBP on a graph processor, enabling real-time inference on dynamically changing and heterogeneous factor graphs. GBP provides a structure-agnostic iteration process that proves advantageous over direct optimization techniques, particularly when managing complex graph topologies without predefined sparseness patterns.

Technical Approach

The technical implementation involves several distinct stages. Initially, scene elements and constraints are identified and proposed by a neural network, such as PlaneRCNN, which segments potential planar regions in images. These hypotheses are then tested within the SLAM system's factor graph. The system constantly performs inference using GBP, which operates through distributed message passing to refine hypotheses and maintain a coherent map. Confirmed elements are integrated into the SLAM map, replacing raw point data to enhance representational density.

Moreover, the authors introduce a dynamic routing system on the graph processor facilitating inference on evolving graph structures. This system ensures the efficient distribution and communication between computational nodes, bypassing the need for time-consuming recompilation of the communication patterns.

Experimental Results

The experiments confirm the efficacy of this distributed SLAM framework. Compared to traditional methods, the proposed system achieves faster convergence rates, with computational complexity remaining largely unaffected by the introduction of additional factor types. In scenarios with planar environments, the system exhibits significant graph compression, alongside enhanced map tracking precision, as evidenced by evaluations against the TUM dataset.

Parallel implementations on specialized hardware like Graphcore's IPU highlight the potential of GBP to exploit the hardware's massive parallelism, providing tangible concurrency benefits over existing SLAM implementations.

Implications and Future Directions

The implications of this research are significant for fields reliant on autonomous understanding and mapping of environments, such as robotics and autonomous vehicles. By providing a more efficient approach to real-time inference in dynamic SLAM graphs, the research presents opportunities to broaden the scope of environments where SLAM systems can be effectively applied.

The paper points towards several future research directions, including the exploration of other abstract feature detectors beyond planar abstractions and the integration of a distributed tracking mechanism within the GBP framework. More broadly, the research ignites interest in developing novel processor architectures tailored to optimize the unique computational demands posed by dynamically evolving factor graphs.

In summary, the paper provides a compelling advance in SLAM research, offering a robust framework that effectively balances semantic richness and computational efficiency through innovative use of Gaussian Belief Propagation and distributed processing.