Dynamic Objects Detection and Tracking - Revision history

BjornAstrand at 14:22, 22 September 2017

2017-09-22T14:22:48Z

Saesha at 10:31, 26 October 2016

2016-10-26T10:31:10Z

Saesha: Created page with "{{StudentProjectTemplate |Summary=Dynamic Objects Detection and Tracking in Warehouses, Using 3D Sensors. |Programme=Mobile and Autonomous Systems |Keywords=3D Sensor, Point C..."

2014-11-21T17:04:07Z

Created page with "{{StudentProjectTemplate |Summary=Dynamic Objects Detection and Tracking in Warehouses, Using 3D Sensors. |Programme=Mobile and Autonomous Systems |Keywords=3D Sensor, Point C..."

New page

{{StudentProjectTemplate
|Summary=Dynamic Objects Detection and Tracking in Warehouses, Using 3D Sensors.
|Programme=Mobile and Autonomous Systems
|Keywords=3D Sensor, Point Cloud, Obstacle Detection, Obstacle Tracking, Obstacle Avoidance.
|TimeFrame=January 2015 until June 2015, with possible extension until September 2015.
|References=Petrovskaya, Anna, and Sebastian Thrun. "Model based vehicle detection and tracking for autonomous urban driving." Autonomous Robots 26.2-3 (2009): 123-139.

Wojke, N.; Haselich, M., "Moving vehicle detection and tracking in unstructured environments," Robotics and Automation (ICRA), 2012 IEEE International Conference on , vol., no., pp.3082,3087, 14-18 May 2012.

Moras, J.; Cherfaoui, V.; Bonnifait, P., "A lidar perception scheme for intelligent vehicle navigation," Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on , vol., no., pp.1809,1814, 7-10 Dec. 2010

Golovinskiy, Aleksey, Vladimir G. Kim, and Thomas Funkhouser. "Shape-based recognition of 3D point clouds in urban environments." Computer Vision, 2009 IEEE 12th International Conference on. IEEE, 2009.

Granstrom, K.; Lundquist, C.; Gustafsson, F.; Orguner, U., "Random Set Methods: Estimation of Multiple Extended Objects," Robotics & Automation Magazine, IEEE , vol.21, no.2, pp.73,82, June 2014

Data Association and Tracking a survey RoboEarth.

Rusu, Radu Bogdan, and Steve Cousins. "3d is here: Point cloud library (pcl)." Robotics and Automation (ICRA), 2011 IEEE International Conference on. IEEE, 2011.

Brostow, Gabriel J., et al. "Segmentation and recognition using structure from motion point clouds." Computer Vision–ECCV 2008. Springer Berlin Heidelberg, 2008. 44-57.

Drost, Bertram, et al. "Model globally, match locally: Efficient and robust 3D object recognition." Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010.

Biasotti, S. ; Falcidieno, B. ; Giorgi, D. ; Spagnuolo, M. “Mathematical Tools for Shape Analysis and Description”, 2014, Publisher :Morgan & Claypool, Edition:1, ISBN:1627053646

Börcs, Attila, et al. "A Model-based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds." (2014).
|Prerequisites=Familiarity with filtering techniques (eg. EKF) for mobile robots localization, Image analysis, programming skills (preferably C++ or Python).
|Supervisor=Björn Åstrand, Saeed Gholami Shahbandi,
|Level=Master
|Status=Open
}}
concise description: This project [as a subset of AIMS project], targets the automation of lift trucks in warehouse environments. Operating automatic guided vehicles in this particular environment is challenging due to the high expected throughput and consequently high traffic. Lift trucks are heavy vehicles operating with relatively high speed in an environment where neither the trucks, nor the humans are well protected as a regular urban traffic. This calls for a extra security measure and cautious decisions. Collision avoidance is an essential skill for mobile robots to guarantee a safe operation in a workspace shared with humans. This project focuses on detection and tracking of dynamic objects in order to avoid collision.

RQ: How to reliably detect, segment, and track dynamic objects (eg. humans and lift trucks) from a 3D point cloud, acquired by the means of a 3D sensor mounted on a mobile robot, in highly structured environment (warehouse).

WP1: startup: literature review and data acquisition
WP2: point cloud manipulation, object segmentation, scene understanding.
WP3: filtering and tracking.
WP4: [bonus] object recognition
Deliverable: an implementation and demonstration of the developed method for detection and tracking of the moving obstacle over the real data acquired in a real warehouse.
[bonus] conference publication (ETFA, ECMR, TAROS)

@@ Line 3: / Line 3: @@
 |Programme=Mobile and Autonomous Systems
 |Keywords=3D Sensor, Point Cloud, Obstacle Detection, Obstacle Tracking, Obstacle Avoidance.
-|TimeFrame=January 2015 until June 2015,  with possible extension until September 2015.
+|TimeFrame=October 2017 to June 2018, with possible extension to September 2018
 |References=Petrovskaya, Anna, and Sebastian Thrun. "Model based vehicle detection and tracking for autonomous urban driving." Autonomous Robots 26.2-3 (2009): 123-139.
@@ Line 26: / Line 26: @@
 Börcs, Attila, et al. "A Model-based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds." (2014).
 |Prerequisites=Familiarity with filtering techniques (eg. EKF) for mobile robots localization, Image analysis, programming skills (preferably C++ or Python).
-|Supervisor=Björn Åstrand, Saeed Gholami Shahbandi,
+|Supervisor=Björn Åstrand, Naveed Muhammad,
 |Level=Master
 |Status=Open

@@ Line 26: / Line 26: @@
 Börcs, Attila, et al. "A Model-based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds." (2014).
 |Prerequisites=Familiarity with filtering techniques (eg. EKF) for mobile robots localization, Image analysis, programming skills (preferably C++ or Python).
 |Supervisor=Björn Åstrand, Saeed Gholami Shahbandi,
 |Level=Master
 |Status=Open
 }}
-concise description: This project [as a subset of AIMS project], targets the automation of lift trucks in warehouse environments. Operating automatic guided vehicles in this particular environment is challenging due to the high expected throughput  and consequently high traffic. Lift trucks are heavy vehicles operating with relatively high speed in an environment where neither the trucks, nor the humans are well protected as a regular urban traffic. This calls for a extra security measure and cautious decisions. Collision avoidance is an essential skill for mobile robots to guarantee a safe operation in a workspace shared with humans. This project focuses on detection and tracking of dynamic objects in order to avoid collision.
+; Concise Description
-RQ: How to reliably detect, segment, and track dynamic objects (eg. humans and lift trucks) from a 3D point cloud, acquired by the means of a 3D sensor mounted on a mobile robot, in  highly structured environment (warehouse).
+; Objective
-WP1: startup: literature review and data acquisition
+; Preliminary Plan
-WP2: point cloud manipulation, object segmentation, scene understanding.
+* startup: literature review and data acquisition
-WP3: filtering and tracking.
+* point cloud manipulation, object segmentation, scene understanding.
-WP4: [bonus] object recognition
+* filtering and tracking.
-Deliverable: an implementation and demonstration of the developed method for detection and tracking of the moving obstacle over the real data acquired in a real warehouse.
+* [bonus] object recognition
-[bonus] conference publication (ETFA, ECMR, TAROS)