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LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry (2401.01887v2)

Published 3 Jan 2024 in cs.CV

Abstract: Visual odometry estimates the motion of a moving camera based on visual input. Existing methods, mostly focusing on two-view point tracking, often ignore the rich temporal context in the image sequence, thereby overlooking the global motion patterns and providing no assessment of the full trajectory reliability. These shortcomings hinder performance in scenarios with occlusion, dynamic objects, and low-texture areas. To address these challenges, we present the Long-term Effective Any Point Tracking (LEAP) module. LEAP innovatively combines visual, inter-track, and temporal cues with mindfully selected anchors for dynamic track estimation. Moreover, LEAP's temporal probabilistic formulation integrates distribution updates into a learnable iterative refinement module to reason about point-wise uncertainty. Based on these traits, we develop LEAP-VO, a robust visual odometry system adept at handling occlusions and dynamic scenes. Our mindful integration showcases a novel practice by employing long-term point tracking as the front-end. Extensive experiments demonstrate that the proposed pipeline significantly outperforms existing baselines across various visual odometry benchmarks.

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Citations (10)
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Summary

  • The paper presents LEAP-VO, which integrates anchor-based dynamic point tracking with temporal probabilistic methods for robust visual odometry.
  • The methodology leverages continuous multi-frame tracking to mitigate challenges like occlusions and low-texture regions, enhancing trajectory estimation.
  • Experimental results demonstrate significant accuracy gains in dynamic scenes, outperforming baseline VO systems in both translation and rotation metrics.

Introduction to Visual Odometry

Visual odometry (VO) is a method used to estimate the motion of a camera by analyzing a series of images captured by the camera as it moves through an environment. It is particularly crucial for applications such as robotics, augmented reality (AR), and autonomous vehicles. The performance of VO systems largely depends on their ability to track points in the image sequence and the accuracy with which these points can be associated across multiple frames.

Challenges of Visual Odometry

Traditional VO methods often employ a two-view approach to match features between consecutive image pairs, but this can be limiting because it ignores the rich temporal information in the sequence of images. Moreover, occlusions, dynamic scenes, and areas with little texture can pose significant challenges to existing VO systems, causing a degradation in their performance.

LEAP and Anchor-Based Dynamic Track Estimation

In response to these challenges, a module named Long-term Effective Any Point Tracking (LEAP) was developed. It combines visual, inter-track, and temporal cues along with strategically selected points called anchors, to provide a robust tracking capability across multiple frames. These anchors help in capturing the global motion patterns by being well-distributed and easy to track. LEAP also introduces a temporal probabilistic approach, which allows for iterative refinement and reasoning about point-wise uncertainty, reflecting how confident the system is in the measurements it has taken.

LEAP-VO: A System for Dynamic Environments

LEAP-VO is a system built around the LEAP module. Its purpose is to integrate long-term point tracking into the process of visual odometry, allowing for continuous motion estimation from dynamic scenes. This multidimensional approach makes LEAP-VO particularly efficient in handling complex scenarios, such as those with moving objects and partial occlusions.

Experiments and Results

Extensive tests were carried out to compare LEAP-VO against other state-of-the-art VO systems. These included indoor, static scenes, and outdoor, dynamic environments. The testing took into consideration factors such as absolute trajectory errors and the relative errors in both translation and rotation.

The results revealed that LEAP-VO achieved significant performance improvements over existing baselines, most notably in dynamic scenes—which are traditionally more challenging for VO systems. These findings suggested that LEAP-VO's innovative integration of temporal information and its robust handling of point tracking made it a more effective solution for dynamic situations.

Potential and Conclusion

LEAP-VO has opened up new avenues for more reliable visual odometry, particularly in environments that involve complex motions and require robust occlusion handling. The system could potentially be adapted and integrated with other long-term point tracking methods to further enhance its camera-tracking accuracy and overall robustness, paving the way for advancements across a range of applications that rely on visual odometry.

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