Accurate and Robust Dense RGB-D SLAM with Neural Implicit Mapping and Self-Supervised Feature Tracking

核心概念

NeSLAM is an advanced dense RGB-D SLAM framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views through neural implicit scene representation, depth completion and denoising, and self-supervised feature tracking.

要約

NeSLAM is a dense RGB-D SLAM system that combines neural implicit scene representation with robust camera tracking and mapping. The key highlights are:

Depth Completion and Denoising Network: A depth completion and denoising network is designed to provide dense and accurate depth images along with depth uncertainty, which guides the neural point sampling and improves geometric consistency.

Hierarchical Scene Representation with SDF: The occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis.

NeRF-Based Self-Supervised Feature Tracking: A NeRF-based self-supervised feature tracking algorithm is proposed for accurate and robust real-time camera tracking in large and complex indoor environments.

Optimization Framework: The system jointly optimizes the scene implicit representation and camera pose estimation through carefully designed loss functions, including depth, color, and ICP losses.

Experiments on various indoor datasets demonstrate the effectiveness and accuracy of NeSLAM in reconstruction, tracking quality, and novel view synthesis compared to recent implicit mapping SLAM systems.

統計

The system uses sparse and noisy depth images as input.
The depth completion and denoising network estimates dense depth images and depth uncertainty.
The hierarchical scene representation uses Signed Distance Field (SDF) values.

引用

"The depth completion and denoising network aims to generate dense and precise depth images with depth uncertainty images. This geometry prior information plays a crucial role in guiding neural point sampling and optimizing the neural implicit representation."
"We propose a NeRF-based self-supervised feature tracking method for accurate and robust camera tracking in large and complex indoor environments, which is proven effectiveness and robust in our experiments."

抽出されたキーインサイト

NeSLAM

by Tianchen Den... 場所 arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20034.pdf

深掘り質問

How can the proposed self-supervised feature tracking network be extended to handle dynamic environments and moving objects

The proposed self-supervised feature tracking network can be extended to handle dynamic environments and moving objects by incorporating motion prediction and object tracking techniques. One approach could be to integrate optical flow algorithms to predict the movement of keypoints between frames, allowing the network to adapt to dynamic scenes. Additionally, object detection and tracking algorithms can be used to identify and track moving objects in the environment, enabling the network to differentiate between static scene elements and dynamic objects. By combining these techniques, the network can learn to adjust its feature tracking based on the movement of objects in the scene, ensuring accurate and robust tracking in dynamic environments.

What are the potential limitations of the SDF-based scene representation, and how could it be further improved to handle more complex geometry

One potential limitation of SDF-based scene representation is its sensitivity to noise and inaccuracies in depth measurements, which can lead to errors in geometry reconstruction. To improve the handling of more complex geometry, the SDF-based representation can be further enhanced by incorporating adaptive resolution grids to capture fine details in the scene. Additionally, integrating uncertainty estimation in the SDF values can help in modeling ambiguous or uncertain regions in the scene geometry. Furthermore, exploring hybrid representations that combine SDF with other geometric representations, such as voxel grids or point clouds, can provide a more comprehensive and robust representation of complex geometry.

Could the depth completion and denoising network be adapted to leverage additional sensor modalities, such as inertial measurements, to enhance the depth estimation in challenging scenarios

The depth completion and denoising network can be adapted to leverage additional sensor modalities, such as inertial measurements, to enhance depth estimation in challenging scenarios. By fusing inertial measurements with depth data, the network can improve depth estimation accuracy in dynamic environments or scenarios with rapid camera movements. Inertial measurements can provide valuable information about camera motion and orientation, which can be used to refine depth estimates and reduce errors caused by motion blur or camera shake. By incorporating inertial measurements into the network architecture, the depth completion and denoising network can enhance its robustness and accuracy in challenging scenarios.