Design and implementation of Camera Based Object Tracking in 3D Space

Object tracking is the task of capturing the 3D position and pose of an object from frame to frame.In this paper we have presented an application based on gesture to control robotic arm through human arm.This arm is based on ATmega16.It is a servo motor based robotic arm with multiple degrees of freedom and having capability to rotate at maximum point in region.This microcontroller based servo motor controller will be controlled through computer system in order to control the individual motor on the basis of provided angle. The Vb.net designed software processing approach has created human computer interaction in order to control hardware baesd robotic arm in 3D co-ordinates

Real-Time Physics-Based Object Pose Tracking during Non-Prehensile Manipulation

We propose a method to track the 6D pose of an object over time, while the object is under non-prehensile manipulation by a robot. At any given time during the manipulation of the object, we assume access to the robot joint controls and an image from a camera looking at the scene. We use the robot joint controls to perform a physics-based prediction of how the object might be moving. We then combine this prediction with the observation coming from the camera, to estimate the object pose as accurately as possible. We use a particle filtering approach to combine the control information with the visual information. We compare the proposed method with two baselines: (i) using only an image-based pose estimation system at each time-step, and (ii) a particle filter which does not perform the computationally expensive physics predictions, but assumes the object moves with constant velocity. Our results show that making physics-based predictions is worth the computational cost, resulting in more accurate tracking, and estimating object pose even when the object is not clearly visible to the camera

Sparse-Dense Motion Modelling and Tracking for Manipulation without Prior Object Models

This work presents an approach for modelling and tracking previously unseen objects for robotic grasping tasks. Using the motion of objects in a scene, our approach segments rigid entities from the scene and continuously tracks them to create a dense and sparse model of the object and the environment. While the dense tracking enables interaction with these models, the sparse tracking makes this robust against fast movements and allows to redetect already modelled objects. The evaluation on a dual-arm grasping task demonstrates that our approach 1) enables a robot to detect new objects online without a prior model and to grasp these objects using only a simple parameterisable geometric representation, and 2) is much more robust compared to the state of the art methods.Comment: IEEE Robotics and Automation Letters (RA-L) 202

Modelling the Xbox 360 Kinect for visual servo control applications

A research report submitted to the faculty of Engineering and the built environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. Johannesburg, August 2016There has been much interest in using the Microsoft Xbox 360 Kinect cameras for visual servo control applications. It is a relatively cheap device with expected shortcomings. This work contributes to the practical considerations of using the Kinect for visual servo control applications. A comprehensive characterisation of the Kinect is synthesised from existing literature and results from a nonlinear calibration procedure. The Kinect reduces computational overhead on image processing stages, such as pose estimation or depth estimation. It is limited by its 0.8m to 3.5m practical depth range and quadratic depth resolution of 1.8mm to 35mm, respectively. Since the Kinect uses an infra-red (IR) projector, a class one laser, it should not be used outdoors, due to IR saturation, and objects belonging to classes of non- IR-friendly surfaces should be avoided, due to IR refraction, absorption, or specular reflection. Problems of task stability due to invalid depth measurements in Kinect depth maps and practical depth range limitations can be reduced by using depth map preprocessing and activating classical visual servoing techniques when Kinect-based approaches are near task failure.MT201

Towards Advanced Robotic Manipulations for Nuclear Decommissioning

Despite enormous remote handling requirements, remarkably very few robots are being used by the nuclear industry. Most of the remote handling tasks are still performed manually, using conventional mechanical master‐slave devices. The few robotic manipulators deployed are directly tele‐operated in rudimentary ways, with almost no autonomy or even a pre‐programmed motion. In addition, majority of these robots are under‐sensored (i.e. with no proprioception), which prevents them to use for automatic tasks. In this context, primarily this chapter discusses the human operator performance in accomplishing heavy‐duty remote handling tasks in hazardous environments such as nuclear decommissioning. Multiple factors are evaluated to analyse the human operators’ performance and workload. Also, direct human tele‐operation is compared against human‐supervised semi‐autonomous control exploiting computer vision. Secondarily, a vision‐guided solution towards enabling advanced control and automating the under‐sensored robots is presented. Maintaining the coherence with real nuclear scenario, the experiments are conducted in the lab environment and results are discussed

TractorEYE: Vision-based Real-time Detection for Autonomous Vehicles in Agriculture

Agricultural vehicles such as tractors and harvesters have for decades been able to navigate automatically and more efficiently using commercially available products such as auto-steering and tractor-guidance systems. However, a human operator is still required inside the vehicle to ensure the safety of vehicle and especially surroundings such as humans and animals. To get fully autonomous vehicles certified for farming, computer vision algorithms and sensor technologies must detect obstacles with equivalent or better than human-level performance. Furthermore, detections must run in real-time to allow vehicles to actuate and avoid collision.This thesis proposes a detection system (TractorEYE), a dataset (FieldSAFE), and procedures to fuse information from multiple sensor technologies to improve detection of obstacles and to generate a map. TractorEYE is a multi-sensor detection system for autonomous vehicles in agriculture. The multi-sensor system consists of three hardware synchronized and registered sensors (stereo camera, thermal camera and multi-beam lidar) mounted on/in a ruggedized and water-resistant casing. Algorithms have been developed to run a total of six detection algorithms (four for rgb camera, one for thermal camera and one for a Multi-beam lidar) and fuse detection information in a common format using either 3D positions or Inverse Sensor Models. A GPU powered computational platform is able to run detection algorithms online. For the rgb camera, a deep learning algorithm is proposed DeepAnomaly to perform real-time anomaly detection of distant, heavy occluded and unknown obstacles in agriculture. DeepAnomaly is -- compared to a state-of-the-art object detector Faster R-CNN -- for an agricultural use-case able to detect humans better and at longer ranges (45-90m) using a smaller memory footprint and 7.3-times faster processing. Low memory footprint and fast processing makes DeepAnomaly suitable for real-time applications running on an embedded GPU. FieldSAFE is a multi-modal dataset for detection of static and moving obstacles in agriculture. The dataset includes synchronized recordings from a rgb camera, stereo camera, thermal camera, 360-degree camera, lidar and radar. Precise localization and pose is provided using IMU and GPS. Ground truth of static and moving obstacles (humans, mannequin dolls, barrels, buildings, vehicles, and vegetation) are available as an annotated orthophoto and GPS coordinates for moving obstacles. Detection information from multiple detection algorithms and sensors are fused into a map using Inverse Sensor Models and occupancy grid maps. This thesis presented many scientific contribution and state-of-the-art within perception for autonomous tractors; this includes a dataset, sensor platform, detection algorithms and procedures to perform multi-sensor fusion. Furthermore, important engineering contributions to autonomous farming vehicles are presented such as easily applicable, open-source software packages and algorithms that have been demonstrated in an end-to-end real-time detection system. The contributions of this thesis have demonstrated, addressed and solved critical issues to utilize camera-based perception systems that are essential to make autonomous vehicles in agriculture a reality