Learning-based Localizability Estimation for Robust LiDAR Localization (2203.05698v2)

Abstract: LiDAR-based localization and mapping is one of the core components in many modern robotic systems due to the direct integration of range and geometry, allowing for precise motion estimation and generation of high quality maps in real-time. Yet, as a consequence of insufficient environmental constraints present in the scene, this dependence on geometry can result in localization failure, happening in self-symmetric surroundings such as tunnels. This work addresses precisely this issue by proposing a neural network-based estimation approach for detecting (non-)localizability during robot operation. Special attention is given to the localizability of scan-to-scan registration, as it is a crucial component in many LiDAR odometry estimation pipelines. In contrast to previous, mostly traditional detection approaches, the proposed method enables early detection of failure by estimating the localizability on raw sensor measurements without evaluating the underlying registration optimization. Moreover, previous approaches remain limited in their ability to generalize across environments and sensor types, as heuristic-tuning of degeneracy detection thresholds is required. The proposed approach avoids this problem by learning from a collection of different environments, allowing the network to function over various scenarios. Furthermore, the network is trained exclusively on simulated data, avoiding arduous data collection in challenging and degenerate, often hard-to-access, environments. The presented method is tested during field experiments conducted across challenging environments and on two different sensor types without any modifications. The observed detection performance is on par with state-of-the-art methods after environment-specific threshold tuning.

• The paper presents a learning-based method that estimates localizability directly from 3D LiDAR data, eliminating the need for environment-specific heuristic thresholds.
• It employs a ResUNet-based sparse 3D convolutional network to extract robust features, allowing seamless generalization across different sensors and settings.
• Field trials using a Velodyne-equipped quadruped robot demonstrate reliable detection of non-localizability in varied scenarios, supporting improved autonomous navigation.

Learning-based Localizability Estimation for Robust LiDAR Localization

This paper addresses a critical aspect of LiDAR-based localization — handling the challenges posed by environments that lack sufficient geometric constraints. Typically, these scenarios, such as tunnels or corridor-like structures, can lead to localization failures due to the symmetric or planar nature of the surroundings, which do not provide enough distinctive features for accurate alignment. Traditionally, methods for detecting such localization failures rely on heuristic thresholds, which are not only computationally demanding but also environment-specific, thus requiring re-tuning when conditions change.

The authors propose a learning-based approach to estimate localizability directly from LiDAR data points. This approach leverages neural networks to predict the feasibility of successful scan-to-scan registration without requiring explicit evaluation of the underlying geometric registration optimization. One of the significant novelties of this work is its ability to generalize across different environments and sensor types without the need for manual adjustments. This is achieved by training the neural network on a diverse set of simulated environments, thus eliminating the necessity for heuristic over-tuning. The use of simulated environments also circumvents the practical difficulties and potential risks of data collection in real, often inaccessible environments.

The network architecture is designed to work with 3D point cloud data and operates end-to-end. Utilization of sparse 3D convolutional neural networks based on the ResUNet architecture allows for efficient processing of point cloud data and robust feature extraction. Through its evaluation, the proposed system achieves a performance on par with current state-of-the-art methods but with significant advantages in terms of flexibility and ease of deployment.

Field trials are performed using the ANYmal-C quadruped robot equipped with a Velodyne VLP-16 sensor. These trials span a variety of environments, including a symmetric underground tunnel, large open fields, and indoor office spaces. The proposed system consistently demonstrated its ability to detect non-localizability across these diverse scenarios effectively. Furthermore, it is shown that this methodology can operate across different LiDAR sensors and maintain reliable estimation, notably displaying no performance degradation when switching sensor types.

In practical terms, this method enables improved multi-modal sensor fusion by providing early detection of localization degeneracy. This can be crucial in robotic applications where reliability and robustness are vital, such as autonomous exploration in subterranean or urban environments. The results indicate that the approach can complement existing systems by offering predictive insights into localization capability, thus potentially mitigating the risk of failures during operation.

The implications of this work suggest that leveraging learning-based approaches for localizability estimation could become a standard piece of future robotic SLAM systems, effectively abstracting away the need for environment-specific fine-tuning and enabling more seamless adaptation to new scenarios. Looking forward, expanding the framework to predict a full 6-DOF covariance matrix could further enhance its applicability, providing more precise information about the environment's geometrical constraints and enabling even more refined localization techniques.

In conclusion, this paper contributes a robust, adaptable approach to determine LiDAR localizability, paving the way for more resilient, versatile robotic navigation systems in challenging environments. Future extensions might focus on integrating this framework with other sensory modalities for enhanced sensor fusion strategies, ultimately pushing the capabilities of autonomous systems further into complex, real-world applications.