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Low Latency Instance Segmentation by Continuous Clustering for LiDAR Sensors (2311.13976v2)

Published 23 Nov 2023 in cs.CV and cs.RO

Abstract: Low-latency instance segmentation of LiDAR point clouds is crucial in real-world applications because it serves as an initial and frequently-used building block in a robot's perception pipeline, where every task adds further delay. Particularly in dynamic environments, this total delay can result in significant positional offsets of dynamic objects, as seen in highway scenarios. To address this issue, we employ a new technique, which we call continuous clustering. Unlike most existing clustering approaches, which use a full revolution of the LiDAR sensor, we process the data stream in a continuous and seamless fashion. Our approach does not rely on the concept of complete or partial sensor rotations with multiple discrete range images; instead, it views the range image as a single and infinitely horizontally growing entity. Each new column of this continuous range image is processed as soon it is available. Obstacle points are clustered to existing instances in real-time and it is checked at a high-frequency which instances are completed in order to publish them without waiting for the completion of the revolution or some other integration period. In the case of rotating sensors, no problematic discontinuities between the points of the end and the start of a scan are observed. In this work we describe the two-layered data structure and the corresponding algorithm for continuous clustering. It is able to achieve an average latency of just 5 ms with respect to the latest timestamp of all points in the cluster. We are publishing the source code at https://github.com/UniBwTAS/continuous_clustering.

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Authors (2)
  1. Andreas Reich (2 papers)
  2. Mirko Maehlisch (4 papers)

Summary

Overview of "Low Latency Instance Segmentation by Continuous Clustering for Rotating LiDAR Sensors"

The paper "Low Latency Instance Segmentation by Continuous Clustering for Rotating LiDAR Sensors" authored by Andreas Reich and Hans-Joachim Wünsche, addresses the challenge of low-latency instance segmentation for LiDAR point clouds in dynamic environments. This task is crucial for improving the perception pipeline efficiency of autonomous systems, especially in scenarios like highways where the swift identification and localization of objects is key to safety and performance.

Key Contributions

The paper introduces a method that uses continuous clustering of LiDAR points, importantly diverging from conventional approaches that rely on processing the complete rotation of a LiDAR sensor. Instead, this method processes each column of the LiDAR's range image as soon as it is available, facilitating a near-real-time instance segmentation process. This innovation allows for a significant reduction in latency, mitigating the positional errors that arise from motion delays.

The proposed approach involves the development of a two-layered data structure that enables the clustering of incoming data in real-time. The authors detail the conditions essential for handling high-resolution LiDAR data effectively and emphasize the role of a large perceptive field of view (FOV) through a hierarchical clustering framework. They also publish their source code, providing the community with a tool to reproduce and expand upon their work.

Methodology

  1. Continuous Range Image Generation: The process involves generating a 2D range image from LiDAR data to significantly reduce complexity. This allows quicker identification of adjacent points for clustering.
  2. Ground Point Classification: This step is critical for eliminating noise and accurately separating objects from the ground, supporting more effective clustering of obstacle points.
  3. Point Tree Construction: The authors employ a point tree structure to efficiently cluster points. This enables the method to dynamically adapt as new points are acquired, facilitating real-time operation.
  4. Cluster Generation and Publication: The algorithm continuously evaluates and publishes completed clusters, ensuring timely updates to downstream processes.
  5. Heuristic Optimizations: Heuristics expand upon the algorithm's real-time capabilities, allowing reductions in computational demands without substantial loss in clustering performance.

Evaluation and Results

The authors evaluate the algorithm using the SemanticKitti dataset, employing metrics such as under-segmentation entropy (USE) and over-segmentation entropy (OSE). The results demonstrate that their method surpasses other techniques in minimizing over-segmentation, a notable achievement attributed to the method's large FOV and effective clustering strategies.

The practical implications of this research are notable in its real-time capability, with the algorithm achieving an average latency of 3 ms, proving its utility for live operations in dynamic environments. This efficiency ensures safer and more efficient navigation decisions in autonomous driving applications.

Implications and Future Work

The paper's findings are significant for the development of more responsive and accurate autonomous systems. By enabling low-latency instance segmentation, it posits an improvement in perception modules that are foundational for tasks such as multi-object tracking and SLAM.

The authors suggest that further work could explore enhanced parallelization strategies to further mitigate the need for heuristics, thereby unlocking the potential for broader application without sacrificing accuracy or computational feasibility.

In conclusion, this paper presents a technically sound advancement in LiDAR data processing that addresses critical latency challenges, thereby contributing significantly to the field of autonomous systems technology.

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GitHub

  1. GitHub - UniBwTAS/continuous_clustering (103 stars)
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