Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

Service robots have to robustly follow and interact with humans. In this paper, we propose a very fast multi-people tracking algorithm designed to be applied on mobile service robots. Our approach exploits RGB-D data and can run in real-time at very high frame rate on a standard laptop without the need for a GPU implementation. It also features a novel depth-based sub-clustering method which allows to detect people within groups or even standing near walls. Moreover, for limiting drifts and track ID switches, an online learning appearance classifier is proposed featuring a three-term joint likelihood. We compared the performances of our system with a number of state-of-the-art tracking algorithms on two public datasets acquired with three static Kinects and a moving stereo pair, respectively. In order to validate the 3D accuracy of our system, we created a new dataset in which RGB-D data are acquired by a moving robot. We made publicly available this dataset which is not only annotated by hand, but the ground-truth position of people and robot are acquired with a motion capture system in order to evaluate tracking accuracy and precision in 3D coordinates. Results of experiments on these datasets are presented, showing that, even without the need for a GPU, our approach achieves state-of-the-art accuracy and superior speed.

Fast RGB-D people tracking for service robots

This paper proposes a very fast and robust multi-people tracking algorithm suitable for mobile platforms equipped with a RGB-D sensor. Our approach features a novel depth-based sub-clustering method explicitly designed for detecting people within groups or near the background and a three-term joint likelihood for limiting drifts and ID switches. Moreover, an online learned appearance classifier is proposed, that robustly specializes on a track while using the other detections as negative examples. Tests have been performed with data acquired from a mobile robot in indoor environments and on a publicly available dataset acquired with three RGB-D sensors and results have been evaluated with the CLEAR MOT metrics. Our method reaches near state of the art performance and very high frame rates in our distributed ROS-based CPU implementation.

http://robotics.dei.unipd.it/images/Papers/Conferences/munaro_iros12.pdf

Tracking people within groups with RGB-D data

A study was conducted to examine the expectations that younger and older individuals have about domestic robots and how these expectations relate to robot acceptance. In a questionnaire participants were asked to imagine a robot in their home and to indicate how much items representing technology, social partner, and teammate acceptance matched their robot. There were additional questions about how useful and easy to use they thought their robot would be. The dependent variables were attitudinal and intentional acceptance. The analysis of the responses of 117 older adults (aged 65-86) and 60 younger adults (aged 18-25) indicated that individuals thought of robots foremost as performance-directed machines, less so as social devices, and least as unproductive entities. The robustness of the Technology Acceptance Model to robot acceptance was supported. Technology experience accounted for the variance in robot acceptance due to age.

/pdf/attitudinal-and-intentional-acceptance-of-domestic-robots-by-4xyww2fc0d.pdf

Attitudinal and Intentional Acceptance of Domestic Robots by Younger and Older Adults

A wide range of human–robot collaborative applications in diverse domains, such as manufacturing, health care, the entertainment industry, and social interactions, require an autonomous robot to fo...

Person-following by autonomous robots: A categorical overview:

This paper describes a stereo-based person detec- tion and tracking method for a mobile robot that can follow a specific person in dynamic environments. Many previous works on person detection use laser range finders which can provide very accurate range measurements. Stereo-based systems have also been popular, but most of them have not been used for controlling a real robot. We propose a detection method using depth templates of person shape applied to a dense depth image. We also develop an SVM-based verifier for eliminating false positive. For person tracking by a mobile platform, we formulate the tracking problem using the Extended Kalman filter. The robot continuously estimates the position and the velocity of persons in the robot local coordinates, which are then used for appropriately controlling the robot motion. Although our approach is relatively simple, our robot can robustly follow a specific person while recognizing the target and other persons with occasional occlusions. Index Terms— Person detection and tracking, Mobile robot, Stereo.

/pdf/robust-stereo-based-person-detection-and-tracking-for-a-1955fwyxex.pdf

Robust Stereo-Based Person Detection and Tracking for a Person Following Robot

This paper describes a person identification method for a mobile robot which performs specific person following under dynamic complicated environments like a school canteen where many persons exist. We propose a distance-dependent appearance model which is based on scale-invariant feature transform (SIFT) feature. SIFT is a powerful image feature that is invariant to scale and rotation in the image plane and also robust to changes of lighting condition. However, the feature is weak against affine transformations and the identification power will thus be degraded when the pose of a person changes largely. We therefore use a set of images taken from various directions to cope with pose changes. Moreover, the number of SIFT feature matches between the model and an input image will decrease as the person becomes farther away from the camera. Therefore, we also use a distance-dependent threshold. The person following experiment was conducted using an actual mobile robot, and the quality assessment of person identification was performed.

/pdf/visual-person-identification-using-a-distance-dependent-29yh7t61z9.pdf

Visual Person Identification Using a Distance-dependent Appearance Model for a Person Following Robot

This paper describes a person identification technique for a mobile robot which performs specific person following under dynamic complicated environments like a school canteen where many persons exist. We use the SIFT feature for identification of a person, and create the distance dependence appearance model which expects the number of SIFT feature matches based on the distance to a person. The person following experiment was conducted using an actual mobile robot, and the quality assessment of person identification was performed.

A SIFT-Based person identification using a distance-dependent appearance model for a person following robot

This paper describes a robot system which can follow a specific person while avoiding obstacles and other people walking around. This system consists of various functional modules, which are a stereo-based robust person detection and tracking, a laser range finder-based map generation, and an on-line randomized motion planning, implemented on a self-contained wheeled mobile robot. All these modules are implemented using RT-middleware, which enhances software develop- ment, maintenance, and reusability. The implemented system is tested in a reason- ably complex environment with several walking people at a time. The experimental results show the feasibility of the system.

http://www.aisl.cs.tut.ac.jp/~jun/pdffiles/ias2010.pdf

Junji Satake

Papers

Robust Stereo-Based Person Detection and Tracking for a Person Following Robot

Visual Person Identification Using a Distance-dependent Appearance Model for a Person Following Robot

A SIFT-Based person identification using a distance-dependent appearance model for a person following robot

Development of a person following robot and its experimental evaluation

Medical Round Robot – Terapio –