Model-Based Detection and Tracking of Single Moving Object Using Laser Range Finder

Rahman, Abdul Hadi Abd; Zamzuri, Hairi; Mazlan, Saiful Amri; Rahman, Mohd Azizi Abdul

doi:10.1109/isms.2014.102

Cited by 12 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LRFs were filtered to cover 180 • angle and 5 meters in distance for all LRFS to cover focused area of targeted pedestrian. A set of calibration tests were done to ensure the accuracy of the produced data to represent legs [18,19] . The calibration results could not perform a 100% accuracy due to sensor noise of LRFs but it achieved considerably reliable output.…”

Section: Methodsmentioning

confidence: 99%

Pedestrian Detection using Triple Laser Range Finders

Rahman

Ariffin

Sani

et al. 2017

IJECE

Self Cite

View full text Add to dashboard Cite

<p>Pedestrian detection is one of the important features in autonomous ground vehicle (AGV). It ensures the capability for safety navigation in urban environment. Therefore, the detection accuracy became a crucial part which leads to implementation using Laser Range Finder (LRF) for better data representation. In this study, an improved laser configuration and fusion technique is introduced by implementation of triple LRFs in two layers with Pedestrian Data Analysis (PDA) to recognize multiple pedestrians. The PDA integrates various features from feature extraction process for all clusters and fusion of multiple layers for better recognition. The experiments were conducted in various occlusion scenarios such as intersection, closed-pedestrian and combine scenarios. The analysis of the laser fusion and PDA for all scenarios showed an improvement of detection where the pedestrians were represented by various detection categories which solve occlusion issues when low number<br />of laser data were obtained.</p>

show abstract

Section: Methodsmentioning

confidence: 99%

Pedestrian Detection using Triple Laser Range Finders

Rahman

Ariffin

Sani

et al. 2017

IJECE

Self Cite

View full text Add to dashboard Cite

show abstract

“…DATMO is another static point classification algorithm [18], [19], [20]. This field studies how to detect and track moving objects around the ego vehicle.…”

Section: A Classification Of Dynamic Objectsmentioning

confidence: 99%

“…The normal distribution is obtained by calculating the mean and covariance matrix of points, which exist inside the voxel. The calculation of the normal distribution with the static probability is shown in Equation (18) and Equation (19).…”

Section: B Weighted Ndt Scan-matching Based On the Static Probabilitymentioning

confidence: 99%

Robust 3-Dimension Point Cloud Mapping in Dynamic Environment Using Point-Wise Static Probability-Based NDT Scan-Matching

et al. 2020

View full text Add to dashboard Cite

Graph-based simultaneous localization and mapping (SLAM) is one of the methods to generate point cloud maps which are used for various applications in autonomous vehicles. Graph-based SLAM represents the pose of the vehicle as a node and the odometry between two different nodes as an edge. Among the edge generating methods, scan matching, light detection and ranging (LiDAR) based method, can provide an accurate pose between two nodes based on the high distance accuracy of the LiDAR. However, the point cloud in real driving situations contains numerous moving objects, which degrade the scan-matching performance. Therefore, this paper defines the static probability which means the likelihood that an acquired point is from a static object, and proposes the weighted normal distribution transformation (NDT), which is achieved by modifying NDT. Weighted NDT is a scan-matching algorithm which can reflect the static probability of each point as a weight. The odometry from the weighted NDT is utilized for graph construction to generate a robust point cloud map even in a dynamic environment. Finally, the proposed algorithm was compared with the existing object removal algorithms in two areas: dynamic object classification and scan-matching performance. Based on the scan-matching results, the accuracy of the point cloud map generated by the proposed algorithm was evaluated with a reference map using highperformance global navigation satellite system (GNSS). It was confirmed that the proposed algorithm has higher classification accuracy and lower scan-matching error compared with other dynamic object removal methods. The proposed algorithm was able to generate a point cloud map, despite the presence of many dynamic objects, that was similar to a map generated in the absence of dynamic objects in the same environment. INDEX TERMS Static probability, Scan-matching, Weighted NDT, LiDAR characteristic, Graph-based SLAM I. INTRODUCTION Light detection and ranging (LiDAR), which is an essential sensor in autonomous vehicles, has the following two characteristics. First, it can provide precise distance information on vehicles, buildings, and people in the form of points. Second, a LiDAR sensor can represent the surroundings of

show abstract

“…This work helped to reduce the effect of appearance changes and enhance the estimation performance. In addition, the problem of LRF-based detection and tracking of moving objects is also studied in Mendes et al (2004), Azim and Aycard (2012) and Rahman et al (2014). Normally, the grid-based method should iterate all cells of a pre-defined grid map, even there is no obstacle occupied.…”

Section: Introductionmentioning

confidence: 99%

MoDeT: a low-cost obstacle tracker for self-driving mobile robot navigation using 2D-laser scan

Toan

Hoang²,

Jo³

2022

View full text Add to dashboard Cite

Purpose Collision avoidance is considered as a crucial issue in mobile robotic navigation to guarantee the safety of robots as well as working surroundings, especially for humans. Therefore, the position and velocity of obstacles appearing in the working space of the self-driving mobile robot should be observed to help the robot predict the collision and choose traversable directions. This paper aims to propose a new approach for obstacle tracking, dubbed MoDeT. Design/methodology/approach First, all long lines, such as walls, are extracted from the 2D-laser scan and considered as static obstacles (or mapped obstacles). Second, a density-based procedure is implemented to cluster nonwall obstacles. These clusters are then geometrically fitted as ellipses. Finally, the combination of Kalman filter and global nearest-neighbor (GNN) method is used to track obstacles’ position and velocity. Findings The proposed method (MoDeT) is experimentally verified by using an autonomous mobile robot (AMR) named AMR SR300. The MoDeT is found to provide better performance in comparison with previous methods for self-driving mobile robots. Research limitations/implications The robot can only see a part of the object, depending on the light detection and ranging scan view. As a consequence, geometrical features of the obstacle are sometimes changed, especially when the robot is moving fast. Practical implications This proposed method is to serve the navigation and path planning for the AMR. Originality/value (a) Proposing an extended weighted line extractor, (b) proposing a density-based obstacle detection and (c) implementing a combination of methods [in (a) and (b) constant acceleration Kalman and GNN] to obtain obstacles’ properties.

show abstract

Model-Based Detection and Tracking of Single Moving Object Using Laser Range Finder

Cited by 12 publications

References 9 publications

Pedestrian Detection using Triple Laser Range Finders

Pedestrian Detection using Triple Laser Range Finders

Robust 3-Dimension Point Cloud Mapping in Dynamic Environment Using Point-Wise Static Probability-Based NDT Scan-Matching

MoDeT: a low-cost obstacle tracker for self-driving mobile robot navigation using 2D-laser scan

Contact Info

Product

Resources

About