Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving (1807.02062v3)

Abstract: We propose a stereo vision-based approach for tracking the camera ego-motion and 3D semantic objects in dynamic autonomous driving scenarios. Instead of directly regressing the 3D bounding box using end-to-end approaches, we propose to use the easy-to-labeled 2D detection and discrete viewpoint classification together with a light-weight semantic inference method to obtain rough 3D object measurements. Based on the object-aware-aided camera pose tracking which is robust in dynamic environments, in combination with our novel dynamic object bundle adjustment (BA) approach to fuse temporal sparse feature correspondences and the semantic 3D measurement model, we obtain 3D object pose, velocity and anchored dynamic point cloud estimation with instance accuracy and temporal consistency. The performance of our proposed method is demonstrated in diverse scenarios. Both the ego-motion estimation and object localization are compared with the state-of-of-the-art solutions.

• The paper proposes a stereo vision method that integrates 2D object detection with viewpoint classification to infer 3D bounding boxes without direct regression.
• It introduces dynamic object bundle adjustment by fusing semantic measurements with sparse feature correspondences for precise ego-motion and object tracking.
• Evaluated on KITTI and Cityscapes, the approach achieves lower trajectory errors and enhanced temporal consistency compared to traditional SLAM methods.

Semantic 3D Object and Ego-motion Tracking for Autonomous Driving: A Technical Analysis

The paper "Stereo Vision-based Semantic 3D Object and Ego-motion Tracking for Autonomous Driving" by Li, Qin, and Shen addresses the complex task of accurately tracking ego-motion and 3D semantic objects using stereo vision within autonomous driving scenarios. This paper critiques current methods and introduces a novel approach that employs a strategic mixture of ready-to-use 2D detections, viewpoint classification, and semantic inference to mitigate inaccuracies in continuous perception needed for autonomous driving.

A Novel Approach to 3D Object Measurements and Ego-motion Tracking

The work rejects the direct regression of 3D bounding boxes, advocating instead for utilizing easily trainable 2D detection combined with discrete viewpoint classification. This method significantly eases the labeling process by relying solely on 2D images. The research emphasizes that 3D box inference is often challenged by frame-independent results, leading to inconsistency—a critical issue in continuous perception in dynamic environments. To combat this, the authors introduce an object-aware, camera pose tracking mechanism enhanced by their innovative dynamic object bundle adjustment (BA). This approach fuses sparse feature correspondences with a semantic 3D measurement model to produce consistent 3D object pose and velocity estimations while maintaining high temporal coherence.

Methodological Contributions

The paper’s primary contributions are threefold:

1. Light-weight 3D Box Inference Methodology: The authors design a lightweight process for 3D box inference, capitalizing on 2D object detection and viewpoint classification. These inferences guide object reprojection contours as well as occlusion masks for feature extraction, simultaneously forming the semantic measurement inputs integral to subsequent optimization processes.
2. Dynamic Object Bundle Adjustment: They propose a robust dynamic object bundle adjustment technique that tightly integrates semantic and feature measurements, enabling continuous state estimation with instance precision and temporal consistency in challenging environments.
3. Practical Demonstration: The system's effectiveness is demonstrated across various scenarios, highlighting not just theoretical merit but also practical applicability in real-world autonomous driving conditions.

Evaluation and Results

The system is rigorously evaluated on the KITTI and Cityscapes datasets, providing substantial performance improvements in ego-motion estimation and object localization compared to state-of-the-art solutions, such as ORB-SLAM2 and 3DOP. Notably, the proposed method continues to perform robustly even in highly dynamic scenarios, where traditional methods exhibit increased error due to their susceptibility to moving obstacles.

The authors report detailed quantitative results, underscoring their system’s competence in maintaining low Absolute Trajectory Error (ATE) and outperforming comparative techniques in demanding scenarios abundant in dynamic elements. The research succeeds by leveraging a blend of semantic-aided object-awareness and feature geometry constraints, ultimately offering more robust results than isolated SLAM or object detection systems.

Implications and Future Directions

In conclusion, this paper fosters a deeper integration of semantic information into 3D tracking systems, advancing tracking reliability and consistency in autonomous driving technologies. This integration of semantic priors extends the potential for improvement in both practical and theoretical realms, particularly in enhancing the temporal coherence of detected objects and refining camera motion tracking. Future work could investigate full integration optimizations where camera and dynamic object states enhance each other, striving for a holistic system that applies dense, unified models to improve overall perception capabilities.

This exploration underscores future research opportunities in refining computational efficiencies and enhancing semantic model accuracy within the dynamic environments characteristic of autonomous systems, setting a substantial foundation for more intricate SLAM and object detection integrations.