Static map reconstruction and dynamic object tracking for a camera and laser scanner system
01 Jun 2018-Iet Computer Vision (The Institution of Engineering and Technology)-Vol. 12, Iss: 4, pp 384-392
TL;DR: In the proposed method, occluded regions are combined 3D motion models with object appearance to manage difficulties in crowded scenes and an improved automatic calibration is designed to merge image and laser point clouds.
Abstract: The vision-based mobile robot's simultaneous localisation and mapping and navigation capability in dynamic environments are highly problematic elements of robot vision applications. The goal of this study is to reconstruct a static map and track the dynamic object for a camera and laser scanner system. An improved automatic calibration is designed to merge image and laser point clouds. Then, the fusion data is exploited to detect the slowly moved object and reconstruct static map. Tracking-by-detection requires the correct assignment of noisy detection results to object trajectories. In the proposed method, occluded regions are combined 3D motion models with object appearance to manage difficulties in crowded scenes. The proposed method was validated by experimental results gathered in a real environment and on publicly available data.
Citations
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TL;DR: This article proposes an innovative method to track the locations of dynamic objects by combining radio-frequency identification and laser ranging information and uses the clustering results from density-based spatial clustering of applications with noise to continuously track the moving object.
Abstract: Due to the unique and contactless way of identification, radio-frequency identification is becoming an emerging technology for objects tracking. As radio-frequency identification does not provide a...
5 citations
22 Dec 2019
TL;DR: Experimental results on the identical laser scanned dataset demonstrate that the approach of SVM with Hungarian method using particle filter outperforms both the threshold based approach with Hungarians method using Kalman filter and the approach to detect and track movers from laser scanned datasets.
Abstract: Laser scanner takes away the problem of private life conservation as it does not record real world videos except scanned data points. So it shows many benefits over the use of video camera. This paper portrays an approach to detect and track movers from laser scanned datasets. Laser scanned data points from each scan are deemed as a video frame. Blobs are extracted from each frame. Support vector machine (SVM) and Hungarian method along with particle filter are used to get trajectories of movers. Experimental results on the identical laser scanned dataset demonstrate that the approach of SVM with Hungarian method using particle filter outperforms both the threshold based approach with Hungarian method using Kalman filter and the approach of SVM with Hungarian method using Kalman filter.
4 citations
TL;DR: This paper shows that the pose of a 2D LRF can be uniquely determined from the single-shot of a trirectangular trihedron, by using LIDAR data of one scan for the first time.
Abstract: Two dimensional (2D) laser rangefinders (LRF) are widely utilized in various mobilized systems, such as robot, intelligent vehicles (IVs), and mobile mapping. Pose estimation for a 2D LRF has important applications for exterior calibration of multiple sensors, registering light detection and ranging (LIDAR) data from multiple LRFs or from a spinning/nodding LRF, etc. This paper shows that the pose of a 2D LRF can be uniquely determined from the single-shot of a trirectangular trihedron. The pose is estimated by solving a simplified Perspective-Three-Point (P3P) problem and a Three-Point-Registration problem. Compared to the raw noisy LIDAR data, the three control points in P3P problem are estimated from line fitting of the sensed LIDAR data to guarantee good and reliable estimation results. The proposed method has been validated with both simulation and real data. The results show that the method is accurate and practical. To the best of our knowledge, this paper solves the pose estimation of a 2D LRF by using LIDAR data of one scan for the first time.
3 citations
TL;DR: This work starts from an encode-decode network, which receives two range maps provided by a Velodyne HDL-64 laser scanner and outputs dynamic probability of each point, and proposes a 3D fully connected CRF (Conditional Random Field) to improve the segmentation performance.
Abstract: The key of robots operating autonomously in dynamic environments is understanding the dynamic characteristics of objects. This paper aims to detect dynamic objects and reconstruct 3D static maps from consecutive scans of scenes. Our work starts from an encode–decode network, which receives two range maps provided by a Velodyne HDL-64 laser scanner and outputs dynamic probability of each point. Since the soft segmentation produced by the network tends to be smooth, a 3D fully connected CRF (Conditional Random Field) is proposed to improve the segmentation performance. Experiments on both the public datasets and real-word platform demonstrate the effectiveness of our method.
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