DynaHull: Density-centric Dynamic Point Filtering in Point Clouds

TL;DR

Indoor LiDAR-based SLAM struggles with dynamic points that degrade localization and mapping. DynaHull introduces a density-based, post-mapping filter that uses local density and convex hull volumes to separate stationary from dynamic points without requiring prior scans, and segments the map into clusters to adapt to nonuniform density. It delivers superior performance against ERASOR, Removert, OctoMap+SOR, and Dynablox across MAE, RMSE, 90th percentile, CD, and EMD, with lower false negatives/positives, at the cost of higher processing time. This approach enhances indoor map accuracy and robot localization in dynamic environments, with potential for further optimization and extension to broader scenarios.

Abstract

In the field of indoor robotics, accurately localizing and mapping in dynamic environments using point clouds can be a challenging task due to the presence of dynamic points. These dynamic points are often represented by people in indoor environments, but in industrial settings with moving machinery, there can be various types of dynamic points. This study introduces DynaHull, a novel technique designed to enhance indoor mapping accuracy by effectively removing dynamic points from point clouds. DynaHull works by leveraging the observation that, over multiple scans, stationary points have a higher density compared to dynamic ones. Furthermore, DynaHull addresses mapping challenges related to unevenly distributed points by clustering the map into smaller sections. In each section, the density factor of each point is determined by dividing the number of neighbors by the volume these neighboring points occupy using a convex hull method. The algorithm removes the dynamic points using an adaptive threshold based on the point count of each cluster, thus reducing the false positives. The performance of DynaHull was compared to state-of-the-art techniques, such as ERASOR, Removert, OctoMap plus SOR, and Dynablox, by comparing each method to the ground truth map created during a low activity period in which only a few dynamic points were present. The results indicated that DynaHull outperformed these techniques in various metrics, noticeably in the Earth Mover's Distance, false negatives, and false positives.

Figures (8)

• Figure 1: Irremovable dynamic points are presented in the color red because there are no stationary objects present behind those points. Meanwhile, the green areas are removed because there is a stationary box behind the observed point.
• Figure 2: Dbot- The robot used for collecting the Data
• Figure 3: Estimating convex hull volume between stationary and dynamic points: The left figure represents the stationary area (wall), while the picture on the right illustrates the dynamic area (human).
• Figure 4: Flowchart of DynaHull method
• Figure 5: Mean and variance of distances between the points in each method compared to the ground truth