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Enhancing Lidar Point Cloud Sampling via Colorization and Super-Resolution of Lidar Imagery

Sier Ha, Honghao Du, Xianjia Yu, Tomi Westerlund

TL;DR

This work addresses drift in LiDAR odometry caused by limited geometric information in dense point clouds and low-resolution lidar imagery. It proposes a DL-based framework that colorizes and upscales lidar imagery to improve keypoint extraction, guiding downsampling for more robust point-cloud registration via LiDAR odometry. By integrating DeOldify for colorization, CARN for super-resolution, and ALIKE for keypoint detection within a KISS-ICP-based LO pipeline, the approach yields improved rotational accuracy and robustness in open environments, with some trade-offs in translation accuracy in tighter spaces. The results suggest a viable path toward calibration-free DL-enhanced LO and SLAM, and highlight a need for lidar-focused colorization and super-resolution models to push performance further.

Abstract

Recent advancements in lidar technology have led to improved point cloud resolution as well as the generation of 360 degrees, low-resolution images by encoding depth, reflectivity, or near-infrared light within each pixel. These images enable the application of deep learning (DL) approaches, originally developed for RGB images from cameras to lidar-only systems, eliminating other efforts, such as lidar-camera calibration. Compared with conventional RGB images, lidar imagery demonstrates greater robustness in adverse environmental conditions, such as low light and foggy weather. Moreover, the imaging capability addresses the challenges in environments where the geometric information in point clouds may be degraded, such as long corridors, and dense point clouds may be misleading, potentially leading to drift errors. Therefore, this paper proposes a novel framework that leverages DL-based colorization and super-resolution techniques on lidar imagery to extract reliable samples from lidar point clouds for odometry estimation. The enhanced lidar images, enriched with additional information, facilitate improved keypoint detection, which is subsequently employed for more effective point cloud downsampling. The proposed method enhances point cloud registration accuracy and mitigates mismatches arising from insufficient geometric information or misleading extra points. Experimental results indicate that our approach surpasses previous methods, achieving lower translation and rotation errors while using fewer points.

Enhancing Lidar Point Cloud Sampling via Colorization and Super-Resolution of Lidar Imagery

TL;DR

This work addresses drift in LiDAR odometry caused by limited geometric information in dense point clouds and low-resolution lidar imagery. It proposes a DL-based framework that colorizes and upscales lidar imagery to improve keypoint extraction, guiding downsampling for more robust point-cloud registration via LiDAR odometry. By integrating DeOldify for colorization, CARN for super-resolution, and ALIKE for keypoint detection within a KISS-ICP-based LO pipeline, the approach yields improved rotational accuracy and robustness in open environments, with some trade-offs in translation accuracy in tighter spaces. The results suggest a viable path toward calibration-free DL-enhanced LO and SLAM, and highlight a need for lidar-focused colorization and super-resolution models to push performance further.

Abstract

Recent advancements in lidar technology have led to improved point cloud resolution as well as the generation of 360 degrees, low-resolution images by encoding depth, reflectivity, or near-infrared light within each pixel. These images enable the application of deep learning (DL) approaches, originally developed for RGB images from cameras to lidar-only systems, eliminating other efforts, such as lidar-camera calibration. Compared with conventional RGB images, lidar imagery demonstrates greater robustness in adverse environmental conditions, such as low light and foggy weather. Moreover, the imaging capability addresses the challenges in environments where the geometric information in point clouds may be degraded, such as long corridors, and dense point clouds may be misleading, potentially leading to drift errors. Therefore, this paper proposes a novel framework that leverages DL-based colorization and super-resolution techniques on lidar imagery to extract reliable samples from lidar point clouds for odometry estimation. The enhanced lidar images, enriched with additional information, facilitate improved keypoint detection, which is subsequently employed for more effective point cloud downsampling. The proposed method enhances point cloud registration accuracy and mitigates mismatches arising from insufficient geometric information or misleading extra points. Experimental results indicate that our approach surpasses previous methods, achieving lower translation and rotation errors while using fewer points.
Paper Structure (17 sections, 3 figures, 3 tables, 1 algorithm)

This paper contains 17 sections, 3 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Point Cloud Sampling
  4. Keypoint Extractor
  5. DL-based Colorization and Super Resolution
  6. Methodology
  7. Dataset for Evaluation
  8. Proposed Point Cloud Sampling and its Evaluation
  9. Overall Pipline
  10. Combination of Super Resolution and Colorization
  11. Selection of DL-based Methods
  12. Point Cloud Registration
  13. Evaluation Metrics
  14. Evaluation Scheme
  15. Hardware Information
  16. ...and 2 more sections

Figures (3)

  • Figure 1: An illustrative example of potential drift and the effectiveness of the proposed approach is presented. In particular, the trajectories of two corners (in the bottom-left and top-right zoomed boxes) show that our approach aligns more closely with the ground truth while utilizing fewer points.
  • Figure 2: Visualization of Ouster lidar data. From top to bottom on the left: signal intensity, reflectivity, near-infrared (near-IR), and depth image. The right side displays the corresponding point cloud.
  • Figure 3: The system overview of the proposed approach. Rng and Sng denote the range and signal images, respectively. The subscript _C indicates that colorization has been applied to the corresponding image type, while _2X signifies the application of 2× super-resolution enhancement.