Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation (2105.07593v2)

Abstract: Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar robot navigation in previously unseen indoor environments. SLAM-net encodes a particle filter based SLAM algorithm in a differentiable computation graph, and learns task-oriented neural network components by backpropagating through the SLAM algorithm. Because it can optimize all model components jointly for the end-objective, SLAM-net learns to be robust in challenging conditions. We run experiments in the Habitat platform with different real-world RGB and RGB-D datasets. SLAM-net significantly outperforms the widely adapted ORB-SLAM in noisy conditions. Our navigation architecture with SLAM-net improves the state-of-the-art for the Habitat Challenge 2020 PointNav task by a large margin (37% to 64% success). Project website: http://sites.google.com/view/slamnet

• The paper introduces a differentiable SLAM-net that embeds a particle filter into the SLAM process to jointly optimize mapping, observation, and transition models.
• It outperforms traditional methods by improving success rates from 37% to 64% on challenging tasks and achieving up to 83.8% with RGB-D inputs.
• The approach paves the way for fully learnable SLAM systems, enhancing robustness in environments with noisy, low-quality, or sparse visual data.

Overview of "Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation"

The paper "Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation" by Peter Karkus et al. presents the Differentiable SLAM Network (SLAM-net), a novel approach designed to address key challenges in simultaneous localization and mapping (SLAM) for visual navigation, particularly in environments with complex conditions such as featureless walls and poor-quality camera inputs. SLAM-net innovates by embedding a SLAM algorithm within a differentiable computation graph, enabling the learning of task-oriented components using backpropagation through the SLAM process. This approach contrasts with traditional SLAM methods that often rely on pre-defined models and sensor-specific algorithms.

Key Technical Contributions

1. Differentiable SLAM Architecture: SLAM-net employs a particle filter-based FastSLAM algorithm encapsulated in a differentiable architecture that allows the joint optimization of all model components. This includes mapping, observation, and transition models learned end-to-end, which significantly enhance robustness under challenging conditions.
2. Experimental Setup and Results: The authors conducted experiments using the Habitat simulation platform, applying SLAM-net to scenarios involving RGB and RGB-D datasets. SLAM-net outperformed several baseline methods, such as ORB-SLAM, under noisy conditions, underscoring the effectiveness of its learned, differentiable components. Notably, SLAM-net improved the success rate from 37% to 64% on the Habitat Challenge 2020 PointNav task.
3. Learning-Based Improvement Over Classic SLAM: A distinguishing aspect of SLAM-net is its ability to learn observation models which were traditionally handcrafted. Incorporating deep learning allows the model to adapt observation models to RGB and RGB-D inputs, traditionally domains requiring handcrafted or sensor-specific models like LiDAR.
4. Localized Mapping with Dynamic Update: The method introduces local mapping mechanisms updated dynamically using a particle filter, which is crucial for handling large-scale SLAM tasks where the environmental model needs incremental refinement.

Numerical Highlights

SLAM-net showcases a substantial success rate (SR) in visual navigation, reaching 83.8% on expert-designed trajectories using RGB-D inputs. Conversely, traditional systems like ORB-SLAM experience a precipitous drop under noisy scenarios, manifesting only a 3.8% SR with the same data. These results establish SLAM-net as a robust solution when high noise levels and challenging indoor environments are involved.

Implications and Future Work

SLAM-net represents a pivotal shift towards incorporating differentiable programming paradigms within the SLAM ecosystem, opening pathways for fully learnable SLAM frameworks. The ability to transcend handcrafted models promises further breakthroughs in environments where traditional feature tracking might fail due to noise, low frame rates, or sparse feature availability. As machine learning and neural networks continue to evolve, systems like SLAM-net may potentially enable more generalized models that require minimal tuning across distinct datasets or conditions.

Future research directions outlined by the authors include extending the differentiable SLAM approach to optimization-based methods and overcoming current limitations in real-world applications. Moreover, exploring multi-task learning setups that align SLAM objectives with other navigation tasks could provide further holistic improvements in autonomous navigation systems. In essence, SLAM-net lays a foundation for next-gen SLAM applications within AI-driven autonomous systems.