DLOAM: Real-time and Robust LiDAR SLAM System Based on CNN in Dynamic Urban Environments

Liu, Wenbo; Sun, Wei; Liu, Yi

doi:10.1109/ojits.2021.3109423

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…The static point cloud after detection will be input into the LOAM [1] framework to enable reliable mapping. Sualeh et al [30] further considered the space-time constraints of sequence frames when training the dynamic object detection model. Both refs.…”

Section: Dynamic Aware 3d Lidar Slammentioning

confidence: 99%

Semantic geometric fusion multi-object tracking and lidar odometry in dynamic environment

Ma,

Jiang,

et al. 2024

Robotica

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Simultaneous localization and mapping systems based on rigid scene assumptions cannot achieve reliable positioning and mapping in a complex environment with many moving objects. To solve this problem, this paper proposes a novel dynamic multi-object lidar odometry (MLO) system based on semantic object recognition technology. The proposed system enables the reliable localization of robots and semantic objects and the generation of long-term static maps in complex dynamic scenes. For ego-motion estimation, the proposed system extracts environmental features that take into account both semantic and geometric consistency constraints. Then, the filtered features can be robust to the semantic movable and unknown dynamic objects. In addition, we propose a new least-squares estimator that uses geometric object points and semantic box planes to realize the multi-object tracking (SGF-MOT) task robustly and precisely. In the mapping module, we implement dynamic semantic object detection using the absolute trajectory tracking list. By using static semantic objects and environmental features, the system eliminates accumulated localization errors and produces a purely static map. Experiments on the public KITTI dataset show that the proposed MLO system provides more accurate and robust object tracking performance and better real-time localization accuracy in complex scenes compared to existing technologies.

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Section: Dynamic Aware 3d Lidar Slammentioning

confidence: 99%

Semantic geometric fusion multi-object tracking and lidar odometry in dynamic environment

Ma,

Jiang,

et al. 2024

Robotica

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“…For instance, Li et al [9] employed ARIMA and hybrid ARIMA models to forecast bus travel time in a congested urban network in China, with the hybrid ARIMA model demonstrating superior prediction accuracy. Similarly, Liu et al [10] successfully utilized an ARIMA model to predict bus travel time in Singapore, highlighting its ability to capture data trends and seasonality for precise short-term predictions. In Beijing, China, Hu et al [11] also leveraged an ARIMA model for bus travel time forecasting, achieving accurate predictions up to 30 min ahead to support real-time bus operations.…”

Section: Comparison Of All Models With Baseline Model Arimamentioning

confidence: 99%

“…Furthermore, we can successfully predict travel times for different types of routes, including both short and long routes. To evaluate the model's performance, we compare it with the autoregressive integrated moving average (ARIMA) time series model, which has been commonly used in previous studies [8][9][10][11]. The experimental results demonstrate the effectiveness of the HTF-NET model in predicting bus travel times.…”

Section: Introductionmentioning

confidence: 99%

Predicting Bus Travel Time in Cheonan City through Deep Learning Utilizing Digital Tachograph Data

Mustafa,

Hwang,

Cho

2024

Electronics

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Urban transportation systems are increasingly burdened by traffic congestion, a consequence of population growth and heightened reliance on private vehicles. This congestion not only disrupts travel efficiency but also undermines productivity and urban resident’s overall well-being. A critical step in addressing this challenge is the accurate prediction of bus travel times, which is essential for mitigating congestion and improving the experience of public transport users. To tackle this issue, this study introduces the Hybrid Temporal Forecasting Network (HTF-NET) model, a framework that integrates machine learning techniques. The model combines an attention mechanism with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) layers, enhancing its predictive capabilities. Further refinement is achieved through a Support Vector Regressor (SVR), enabling the generation of precise bus travel time predictions. To evaluate the performance of the HTF-NET model, comparative analyses are conducted with six deep learning models using real-world digital tachograph (DTG) data obtained from intracity buses in Cheonan City, South Korea. These models includes various architectures, including different configurations of LSTM and GRU, such as bidirectional and stacked architectures. The primary focus of the study is on predicting travel times from the Namchang Village bus stop to the Dongnam-gu Public Health Center, a crucial route in the urban transport network. Various experimental scenarios are explored, incorporating overall test data, and weekday and weekend data, with and without weather information, and considering different route lengths. Comparative evaluations against a baseline ARIMA model underscore the performance of the HTF-NET model. Particularly noteworthy is the significant improvement in prediction accuracy achieved through the incorporation of weather data. Evaluation metrics, including root mean squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE), consistently highlight the superiority of the HTF-NET model, outperforming the baseline ARIMA model by a margin of 63.27% in terms of the RMSE. These findings provide valuable insights for transit agencies and policymakers, facilitating informed decisions regarding the management and optimization of public transportation systems.

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“…D RIVEN by data, computation, and algorithms, deep learning-based vehicle detection methods have been greatly improved in terms of accuracy and robustness [1]. Since the 3D LIDAR point cloud can directly obtain the depth information of the target, the deep learning method based on the 3D LIDAR point cloud has also been developed rapidly [2], [3]. However, such method has encountered the problem of point cloud sparsity, and sparsity increases with distance.…”

Section: Introductionmentioning

confidence: 99%

3-D Vehicle Detection Enhancement Using Tracking Feedback in Sparse Point Clouds Environments

Qian

Wang

Zhuang

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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In recent years, vehicle detection in intelligent transportation systems using 3D LIDAR point clouds based on deep neural networks has made substantial progress. However, when the point clouds are very sparse, the detection model cannot generate proposals efficiently, resulting in false negative results. Considering that the object tracking technology accurately predicts vehicles based on historical measurements and motion models, and these prediction results can become proposals for object detection. Therefore, this paper proposes a novel object detection paradigm based on tracking feedback to address the false negative problem based on sparse point clouds. According to the distribution of the state vector from the Kalman prediction, multiple proposals are sampled and fed back to the second stage of twostage detection models. After regression and non-maximum suppression, the false negative results can be effectively reduced. This method enhances the vehicle detection capability of classical neural networks. Comparing the recall metric of multiple detection models at different distances in the public KITTI and nuSences datasets, the proposed method can promote up to 5.31% compared to the previous method, which reflects the effectiveness and versatility of the proposed method. INDEX TERMS Vehicle detection, tracking feedback, sparse point clouds. YEQIANG QIAN (Member, IEEE) received the Ph.D. degree in control science and engineering from

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DLOAM: Real-time and Robust LiDAR SLAM System Based on CNN in Dynamic Urban Environments

Cited by 6 publications

References 25 publications

Semantic geometric fusion multi-object tracking and lidar odometry in dynamic environment

Semantic geometric fusion multi-object tracking and lidar odometry in dynamic environment

Predicting Bus Travel Time in Cheonan City through Deep Learning Utilizing Digital Tachograph Data

3-D Vehicle Detection Enhancement Using Tracking Feedback in Sparse Point Clouds Environments

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