Observation Time Difference: an Online Dynamic Objects Removal Method for Ground Vehicles

TL;DR

This paper introduces an online method for removing dynamic objects in urban mapping for ground vehicles, improving efficiency and robustness by classifying objects based on observation time differences.

Contribution

It proposes a novel online dynamic objects removal approach using observation time differences, enabling real-time processing and outperforming existing offline methods.

Findings

Reduces processing time per frame by over 60%.

Effective on datasets with highly dynamic objects.

More robust than state-of-the-art methods.

Abstract

In the process of urban environment mapping, the sequential accumulations of dynamic objects will leave a large number of traces in the map. These traces will usually have bad influences on the localization accuracy and navigation performance of the robot. Therefore, dynamic objects removal plays an important role for creating clean map. However, conventional dynamic objects removal methods usually run offline. That is, the map is reprocessed after it is constructed, which undoubtedly increases additional time costs. To tackle the problem, this paper proposes a novel method for online dynamic objects removal for ground vehicles. According to the observation time difference between the object and the ground where it is located, dynamic objects are classified into two types: suddenly appear and suddenly disappear. For these two kinds of dynamic objects, we propose downward retrieval and upward retrieval methods to eliminate them respectively. We validate our method on SemanticKITTI dataset and author-collected dataset with highly dynamic objects. Compared with other state-of-the-art methods, our method is more efficient and robust, and reduces the running time per frame by more than 60$\%$ on average.