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Open Access

ARTICLE

DGMSE: A Real-Time Dynamic Object Removal Framework Based on Detection-Guided Markov State Estimation

Jiahua Kou1, Chengbo Guo1,*, Weiyue Xing1, Zheng Yang1, Jiaxuan Cao1, Shufa Sun1, Yanling Guo2
1 School of Civil Engineering and Transportation, Northeast Forestry University, Harbin, China
2 School of Mechatronics Engineering, Northeast Forestry University, Harbin, China
* Corresponding Author: Chengbo Guo. Email: email
(This article belongs to the Special Issue: Vision, LiDAR, and Sensor Fusion-Based SLAM for Autonomous Navigation)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.081445

Received 02 March 2026; Accepted 26 May 2026; Published online 17 June 2026

Abstract

Dynamic objects in LiDAR SLAM often introduce ghosting artifacts that degrade map quality. While offline methods can successfully clean these maps, they lack real-time capabilities. Conversely, online methods often suffer from state oscillation (where moving objects are misclassified as static when they temporarily stop) and incomplete point cloud removal. To address these challenges, we propose DGMSE, a real-time framework for removing dynamic point clouds in complex urban environments. Our approach consists of three sequential steps. First, the PointPillars 3D detection network quickly isolates potential dynamic objects, significantly reducing computational overhead. Second, to mitigate state oscillation, we introduce a five-state Markov chain to continuously estimate the probabilistic motion state of objects, which is not limited to simple moving-or-static classifications. This temporal memory prevents misclassification during sudden stops or motion transitions. Finally, instead of simply deleting all points inside a detection box, a density-adaptive clustering approach evaluates the geometric and motion characteristics of points, precisely segmenting dynamic objects from the static background without damaging permanent structures. Evaluated on the SemanticKITTI dataset, DGMSE achieves an F1-score of 0.95–0.99, achieving performance competitive with offline methods while satisfying real-time processing requirements.

Keywords

LiDAR SLAM; dynamic object removal; deep learning; state estimation

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