CodeSLAM - Learning a Compact, Optimisable Representation for Dense Visual SLAM

Abstract: The representation of geometry in real-time 3D perception systems continues to be a critical research issue. Dense maps capture complete surface shape and can be augmented with semantic labels, but their high dimensionality makes them computationally costly to store and process, and unsuitable for rigorous probabilistic inference. Sparse feature-based representations avoid these problems, but capture only partial scene information and are mainly useful for localisation only. We present a new compact but dense representation of scene geometry which is conditioned on the intensity data from a single image and generated from a code consisting of a small number of parameters. We are inspired by work both on learned depth from images, and auto-encoders. Our approach is suitable for use in a keyframe-based monocular dense SLAM system: While each keyframe with a code can produce a depth map, the code can be optimised efficiently jointly with pose variables and together with the codes of overlapping keyframes to attain global consistency. Conditioning the depth map on the image allows the code to only represent aspects of the local geometry which cannot directly be predicted from the image. We explain how to learn our code representation, and demonstrate its advantageous properties in monocular SLAM.

• The paper presents a novel compact representation that leverages a variational auto-encoder and U-Net to optimize depth estimation and camera poses in dense visual SLAM.
• It employs synthetic depth training data and multiple overlapping frames to significantly reduce reconstruction error while ensuring computational efficiency.
• Experimental results on datasets like EuRoC and NYU V2 demonstrate accurate 3D reconstructions with fewer keyframes and notable improvements in trajectory error performance.

An Analysis of CodeSLAM: A Compact Representation for Dense Visual SLAM

This paper presents CodeSLAM, an innovative approach to monocular dense SLAM (Simultaneous Localization and Mapping) systems. The researchers explore a method to achieve a balance between the dense representation of 3D geometry and computational efficiency by utilizing a compact code-based representation, which is conditioned on image intensity data. This development is significant in the field of 3D perception systems, where there is often a trade-off between accuracy and real-time performance due to the high dimensionality of dense representations.

Methodology Overview

The authors of CodeSLAM leverage inspiration from auto-encoder networks and depth prediction from images to introduce a novel, compact representation of scene geometry. The proposed system generates depth maps from image data and a learned compact code consisting of a limited number of parameters. Unlike traditional dense SLAM approaches, which often rely on approximate inference due to computational constraints, the compact representation in CodeSLAM allows for rigorous joint optimization of camera poses and geometry from overlapping keyframes.

The process is initiated using a variational auto-encoder architecture that focuses on representing geometry via a code that encodes aspects of the scene not directly inferable from image data. Notably, depth prediction is conditioned on image intensity through the integration of a U-Net for capturing intensity features. This architecture enables the learned compact representation to maintain scene details and optimize them for SLAM applications. Moreover, the authors emphasize the training of their code representation on synthetic depth data from the SceneNet RGB-D dataset, a task carried out through an ADAM optimizer approach.

Numerical Results and Evaluation

The evaluation methodology for CodeSLAM emphasizes both qualitative and quantitative performance metrics. The authors demonstrate the system's capacity to retrieve a dense map of a scene with relatively few keyframes. Experimental results reveal significant refinement in depth estimation accuracy as the system integrates multiple overlapping frames. In two-frame reconstructions using real-world datasets like EuRoC and NYU V2, the system shows successfully estimated geometry, where the primary traits of the scenes are effectively reconstructed.

The impact of additional overlapping frames to the system's refinement capability is illustrated through the reduction of reconstruction error in a series of test frames. Furthermore, the CodeSLAM approach is evaluated within a localized SLAM system set up, focusing on a practical, closed-loop implementation. Demonstrations of the sliding window visual odometry mode, particularly on highly challenging datasets like EuRoC, reveal promising trajectory error performance.

Implications and Future Directions

CodeSLAM introduces a compact, optimizable representation that is a step toward improving real-time dense SLAM systems. This representation can facilitate efficient optimization within constrained computational environments, potentially setting new directions in the integration of deep learning and traditional SLAM methodologies. The ability to achieve joint optimization of geometry and motion in monocular vision systems showcases practical advancements for applications in robotics, autonomous navigation, and augmented reality.

While the paper primarily uses synthetic training data, future efforts could explore fine-tuning on real-world datasets to further enhance the robustness and generalizability of the model. Additionally, extending the framework to address complete scene representations that are not strictly tied to frames or images could further enhance SLAM capabilities, pushing toward seamless 3D structure recognition and object-level understanding.

In conclusion, CodeSLAM represents a noteworthy evolution in efficiently leveraging learned representations for visual SLAM tasks, with potential for substantial impact in both practical applications and ongoing academic research within the computer vision and robotics communities.