DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions (2202.02556v1)

Abstract: We present a novel real-time visual odometry framework for a stereo setup of a depth and high-resolution event camera. Our framework balances accuracy and robustness against computational efficiency towards strong performance in challenging scenarios. We extend conventional edge-based semi-dense visual odometry towards time-surface maps obtained from event streams. Semi-dense depth maps are generated by warping the corresponding depth values of the extrinsically calibrated depth camera. The tracking module updates the camera pose through efficient, geometric semi-dense 3D-2D edge alignment. Our approach is validated on both public and self-collected datasets captured under various conditions. We show that the proposed method performs comparable to state-of-the-art RGB-D camera-based alternatives in regular conditions, and eventually outperforms in challenging conditions such as high dynamics or low illumination.

• The paper introduces DEVO, a real-time visual odometry framework that integrates a depth camera with a high-resolution event sensor to achieve robust pose tracking in challenging environments.
• It employs innovative time-surface maps and semi-dense depth map generation for precise edge alignment, ensuring high accuracy even under rapid motion and low illumination.
• Comparative evaluations demonstrate that DEVO outperforms traditional RGB-D methods in adverse conditions, promising scalability for dynamic indoor robotics applications.

An Analysis of DEVO: Depth-Event Camera Visual Odometry in Challenging Conditions

The paper introduces DEVO, a real-time visual odometry (VO) framework combining a stereo setup of a depth and a high-resolution event camera. DEVO aims to balance accuracy and robustness with computational efficiency, particularly excelling in challenging conditions such as high dynamics or low illumination. This is achieved by extending conventional edge-based semi-dense visual odometry towards time-surface maps derived from event streams. The framework is validated on both public and proprietary datasets, showcasing comparable performance to state-of-the-art RGB-D camera-based alternatives in regular scenarios and superior performance in challenging conditions.

Key Framework Components and Methodology

1. Time-Surface Maps: DEVO utilizes time-surface maps for representing event streams. These maps capture the temporal nature of events, offering a suitable structure for efficient and high-accuracy edge extraction and alignment. This use of time-decay models allows the system to emphasize recent events, which are crucial for detecting and aligning edges accurately.
2. Semi-dense Depth Map Generation: The system generates semi-dense depth maps by warping depth values from the depth camera. This design choice enables robust depth assignment, which is pivotal for accurate 3D reconstructions, especially under occlusions or misalignments typically caused by extrinsic calibration errors.
3. Camera Tracking: The tracking module updates camera poses through efficient geometric semi-dense 3D-2D edge alignment. This involves minimizing an energy function defined by the alignment of a semi-dense point cloud with the current view's negated time-surface map. This alignment process is handled using a Lucas-Kanade method for efficiency.
4. Handling of 6-DoF Motion: DEVO ensures efficient and successful operation across varied conditions, handling six degrees of freedom in motion estimation. The evaluation highlights DEVO's ability to function effectively even under the challenges of high-speed motion or low ambient light.

Results and Comparative Analysis

DEVO is rigorously tested against several contemporary methods. In comparisons with solutions like ESVO, DEVO demonstrates superior performance in diverse environments. It showcases robust handling of noise in event streams, a notable strength over alternatives that struggle with tracking stability under similar conditions. In addition to event-based comparisons, DEVO exhibits equal or superior results compared to RGB-D and depth-only solutions such as KinectFusion and Canny-VO. Notably, DEVO manages better performance in low-light scenarios, a frequent Achilles' heel of many vision-based systems. Moreover, its ability to function at reduced frame rates underscores a significant advantage regarding energy efficiency and computational demand.

Implications and Future Directions

Practically, DEVO offers an attractive solution for indoor mobile robotics where lighting conditions and motion dynamics are inherently variable. Its integrated use of depth and event cameras paves the way for high-performance engagement in these scenarios without incurring the high computational costs typical of other approaches like KinectFusion. On a theoretical level, DEVO demonstrates promising scalability with powerful implications for future research in event-based vision systems. This research sets a precedent allowing for the exploration of enhanced dynamic vision sensor designs and deeper integration of multi-sensor approaches.

The field could observe developments around more compact and affordable event camera systems, aiding broader applicability and real-world deployment of hybrid odometry techniques. Furthermore, advancements in energy-efficient hardware design suited to event camera data processing could enhance the accessibility and sustainability of such frameworks in large-scale implementations. More broadly, DEVO’s methods may spur innovation in the design of autonomous navigation systems, particularly those targeting energy-aware and robust environmental interaction. The groundwork laid by DEVO thus represents a step toward versatile and resilient perception systems, augmenting the ability of AI to operate under previously restrictive conditions.