Camera Motion Estimation from RGB-D-Inertial Scene Flow (2404.17251v1)

Abstract: In this paper, we introduce a novel formulation for camera motion estimation that integrates RGB-D images and inertial data through scene flow. Our goal is to accurately estimate the camera motion in a rigid 3D environment, along with the state of the inertial measurement unit (IMU). Our proposed method offers the flexibility to operate as a multi-frame optimization or to marginalize older data, thus effectively utilizing past measurements. To assess the performance of our method, we conducted evaluations using both synthetic data from the ICL-NUIM dataset and real data sequences from the OpenLORIS-Scene dataset. Our results show that the fusion of these two sensors enhances the accuracy of camera motion estimation when compared to using only visual data.

• The paper introduces a novel RGB-D-inertial fusion method that optimizes camera motion estimation using multi-frame and marginalization techniques.
• The approach leverages direct scene flow from RGB-D data integrated with IMU readings to significantly reduce positional errors.
• Experimental evaluations on synthetic and real datasets demonstrate improved precision and efficiency compared to traditional single-modality methods.

Enhanced Camera Motion Estimation Through RGB-D-Inertial Integration

Introduction

The integration of sensory data for autonomous navigation has distinct advantages in terms of accuracy and robustness. This paper introduces a novel RGB-D-inertial formulation for estimating camera motion in rigid environments. The authors propose a tightly coupled optimization strategy that spans multi-frame scenarios to effectively utilize RGB-D and Inertial Measurement Unit (IMU) data. They introduce a methodological advancement by fusing inertial readings with visual information directly derived from scene flow without prior feature extraction.

Contributions and Methodology

The paper's primary contribution is the fusion method that incorporates inertial data with RGB-D to estimate camera motion. This approach shows marked improvements in the precision of motion estimates under the following setups:

• Multi-Frame Optimization: Leveraging multi-frame data to enhance motion estimation accuracy.
• Marginalization Strategy: Employing a strategic marginalization of older states to refine current predictions and sustain computational efficiency.

The method integrates camera pose estimation through scene flow, emphasizing direct measurement utilization. This approach differentiates itself from prior works which commonly separate feature extraction from inertial integration.

Technical Approach

The technical approach is divided into several parts:

• Scene Flow Estimation: Utilizing RGB-D data to calculate the three-dimensional motion field of scene points.
• IMU Data Integration: Incorporating accelerometer and gyroscope readings to enhance the motion estimation, accounting for device bias and noise.
• Joint Optimization: Deploying a cost function that embeds both the visual residuals (from scene flow) and inertial residuals, balanced by their respective covariances.
• Marginalization: Applying a marginalization process to older states to retain a manageable computation load without significant information loss.

Evaluation

Evaluations were performed on synthetic data from the ICL-NUIM dataset and on real-world data from the OpenLORIS-Scene dataset. Results were compared against standard RGB-D based methods. The proposed method significantly reduced positional error metrics and showed more consistent performance in realistic dynamic settings. Notably, the improvements in error metrics were pronounced when integrating inertial data, which substantiates the benefit of multi-sensory fusion over single-modality systems. Furthermore, the application of state marginalization showcased an ability to maintain or slightly improve the accuracy of the system while managing computational demands effectively.

Theoretical Implications

From a theoretical standpoint, this paper advances understanding of how integration techniques can leverage the complementary nature of RGB-D and inertial data. The formulation highlights the effectiveness of direct optimization methods in the context of motion estimation, setting a foundation for future explorations into more complex or varied environmental interactions.

Speculations on Future AI Developments

Looking forward, the fusion of RGB-D and inertial data could see broader applications in areas requiring high precision navigation under dynamic conditions, such as in autonomous vehicles and robotics in populous environments. Moreover, enhancements in sensor technology or algorithmic efficiency could enable real-time applications in more computationally constrained platforms such as mobile devices and drones. Potential research could also explore the resilience of these methods in adversarial conditions or their adaptation to underwater or aerial navigation scenarios where traditional sensors face limitations.

In conclusion, the proposed RGB-D-inertial integration approach represents a notable advance in sensor-based odometry. It offers a novel perspective on leveraging the intrinsic strengths of each sensor modality to enhance motion estimation accuracy effectively.