End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization (2201.03860v2)

Abstract: Existing learning methods for LiDAR-based applications use 3D points scanned under a pre-determined beam configuration, e.g., the elevation angles of beams are often evenly distributed. Those fixed configurations are task-agnostic, so simply using them can lead to sub-optimal performance. In this work, we take a new route to learn to optimize the LiDAR beam configuration for a given application. Specifically, we propose a reinforcement learning-based learning-to-optimize (RL-L2O) framework to automatically optimize the beam configuration in an end-to-end manner for different LiDAR-based applications. The optimization is guided by the final performance of the target task and thus our method can be integrated easily with any LiDAR-based application as a simple drop-in module. The method is especially useful when a low-resolution (low-cost) LiDAR is needed, for instance, for system deployment at a massive scale. We use our method to search for the beam configuration of a low-resolution LiDAR for two important tasks: 3D object detection and localization. Experiments show that the proposed RL-L2O method improves the performance in both tasks significantly compared to the baseline methods. We believe that a combination of our method with the recent advances of programmable LiDARs can start a new research direction for LiDAR-based active perception. The code is publicly available at https://github.com/vniclas/lidar_beam_selection

End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization

The paper "End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization" addresses the emerging need for an optimized configuration of LiDAR beams in autonomous systems. Traditionally, LiDAR systems use fixed beam configurations that do not adapt to specific tasks, potentially leading to suboptimal performance. The authors propose a reinforcement learning-based framework, denoted as RL-L2O (Reinforcement Learning-Based Learning-to-Optimize), to dynamically determine the optimal distribution of LiDAR beams for different tasks, particularly focusing on 3D object detection and localization.

Methodology and Approach

The core contribution of this paper is the introduction of an end-to-end RL framework that tailors LiDAR beam configurations to improve task-specific performance. The proposed RL-L2O framework is designed to explore the vast solution space of possible beam configurations by employing a parameterized policy that optimizes based on the defined reward function related to task performance. The state space represents different LiDAR beam configurations while the action space consists of possible adjustments to these configurations.

For 3D object detection, the framework is applied to a pipeline using Pseudo-LiDAR data generated from stereo images, with the reward function based on the 3D mean Average Precision (mAP) metric. The RL agent effectively learns configurations that enhance the accuracy of object detection by positioning beams optimally for graph-based depth correction, superseding arbitrary configurations like the equidistant setup.

In localization tasks, such as those using data from the Oxford RobotCar dataset, the agent optimizes the beam selection by focusing on static features of an environment conducive to precise point-cloud localization. Analytics of configurations emanate from comparisons between predicted and actual poses derived from ICP-based registration.

Results and Implications

The results from the experiments show marked improvements in task performance when using the RL-L2O optimized beam configurations compared to baseline configurations. The paper reports up to 2.70% mAP improvement in the moderate difficulty car class for 3D object detection, demonstrating the framework’s capability to fine-tune configurations for enhanced performance.

Furthermore, the framework’s implications extend beyond cost-reduction in using low-resolution LiDAR systems. It suggests a potential shift in LiDAR applications towards more versatile and adaptive systems that can cater to the demands of different environments and tasks without requiring hardware overhauls. This adaptability is particularly crucial for large-scale deployment in autonomous vehicles, where economic constraints can be a significant barrier.

Future Directions

The authors propose the integration of their framework with recent advances in programmable LiDAR technology, which can dynamically adjust beam patterns in real-time. This could open avenues for active perception systems capable of continuously optimizing perception based on environmental context, thus delivering more robust and reliable autonomous systems.

Overall, the paper effectively demonstrates the feasibility and benefits of learning-based optimization of sensor configurations, suggesting further research could explore real-time implementation and adaptations across different sensing platforms beyond LiDAR. Such advancements could vastly improve the efficacy and cost-effectiveness of perception systems across a variety of autonomous applications.