Alexander Carballo

1Km autonomous robot navigation on outdoor pedestrian paths “running the Tsukuba challenge 2007”

This paper presents and describes the approach for achieving long distance autonomous navigation with a mobile robot on outdoor cluttered pedestrian paths. The task was to finish an event launched by the City of Tsukuba in Japan, called ldquoReal World Robot Challengerdquo, of navigating 1km autonomously in a real environment with real pedestrians and bicycles. The hardware, software and strategy for navigating in cluttered environments is explained. Moreover, the complementary functionality of the overall system where map-based and sensor-based navigation seamlessly change, is presented. The robustness of the system is validated with experimental results.

People detection using range and intensity data from multi-layered Laser Range Finders

Effective detection of people is a basic requirement for robot coexistence in human environments. In our previous work [1] we proposed a method for people detection and position estimation using multiple layers of Laser Range Finders (LRF) in a mobile robot. We extend our work by introducing laser reflection intensity as a novel feature for people detection, achieving significant improvement of detection rates. In concrete, we propose a method for calibration of laser intensity data, a method for segment separation using laser intensity, and introduce two new intensity-based features for people detection: the variance of laser intensity and the variance of intensity differences. We present experimental results that confirm the effectiveness of our multi-layered detection method including laser intensity.

Fusion of double layered multiple laser range finders for people detection from a mobile robot

This work proposes a new method people detection and position estimation from a mobile robot by fusion of multiple laser range finders arranged in two layers. Sensors facing opposite directions in a single row (layer) are fused to produce 360deg scan data of robotpsilas surroundings, then data from every layer is further fused to create a 3D model of people and from there their position. The main problem of our research is an autonomous mobile robot acting as member of a people group moving in public areas, simple and accurate people detection and tracking is an important requirement. We present experimental results of fusion steps and people detection in an indoor environment.

Reliable People Detection Using Range and Intensity Data from Multiple Layers of Laser Range Finders on a Mobile Robot

Reliable people detection is an important task in several areas like security, intelligent environments and human robot interaction. People detection does not depend only upon separation of static environment objects from those showing motion (hopefully humans), a reliable system should be able to detect static people even in cluttered environments.This work presents a reliable approach for people detection and position estimation using multiple layers of Laser Range Finders (LRF) on a mobile robot. Each layer combines two LRF sensors to scan around the robot’s surroundings and are vertically separated to detect distinct parts of the human body. By using AdaBoost we create strong classifiers to detect body parts, candidate segments in each layer are fused for people detection, and we use simple data association to estimate their positions. Additionally, this work introduces laser reflection intensity as a novel property for people detection. First, we present a study of laser intensity and textiles, then introduce new intensity-based features for detection, and propose a method for segment separation using laser intensity. We provide a thorough evaluation of our multi-layered system though several experiments on a mobile robot.

Laser reflection intensity and multi-layered Laser Range Finders for people detection

Successful detection of people is a basic requirement for a robot to achieve symbiosis in peoples daily life. Specifically, a mobile robot designed to follow people needs to keep track of peoples position through time, for it defines the robots position and trajectory.

Time synchronization between SOKUIKI sensor and host computer using timestamps

Time is crucial in applications such as sensor data fusion, autonomous mobility and SLAM. However clocks at end systems are rarely synchronized and often running at different speeds. Lack of synchronization therefore reduces the accuracy of sensor readings. The SOKUIKI scanning laser range finder allows acquiring time values by timestamping range readings. Our work consists in a method for time synchronization, with clock skew estimation, between a laser range finder sensor and a host computer.

People Detection using Double Layered Multiple Laser Range Finders by a Companion Robot

Successful detection and tracking of people is a basic requirement to achieve a robot symbiosis in people daily life. Specifically, a mobile robot designed to follow people needs to keep track of people position through time, for it defines the robot’s position and trajectory.