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

Abstract

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.

Development of a Sensor to Measure Physician Consultation Times

“…Specific arrangements with WiFi signals can be also used to detect bi-directional motion [30,31]. Very promising is the expansion from a single point measurement to a scanning range [17,18], which also includes an increased effort in the post processing. Nevertheless, the inclusion of a further sensor type will be considered as part of the future work.…”

“…Far better results could be reached with a time of flight (ToF) distance sensor, which is typically used as a scanning device [12][13][14][15] rang finder [16][17][18] and gesture detection sensor [19]. The used VL53L0X module is capable of capturing distances up to a maximum range of 2 m with a very high accuracy.…”

Motion Detection from Mobile Robots with Fuzzy Threshold Selection in Consecutive 2D Laser Scans

“…To distinguish them from static objects, geometric and statistical features [20], theoretical shape models [21], laser reflection intensity [22] or a threshold [16,17] can be used. The clustering threshold can be chosen empirically as a constant [17] or time-variant value [16].…”

Robust Features for Leg Detection in 2d Laser Range Data

“…This threshold is generally a user-defined value (Weinrich et al, 2014;Mozos et al, 2010;Spinello and Siegwart, 2008;Arras et al, 2007) or determined by a geometric rule (Li. et al, 2018;Borges and Aldon, 2004;Carballo et al, 2011;Kim et al, 2016). * Corresponding author Then, the features of each segment are calculated and used to classify the segment into leg and non-leg.…”

“…Then, Spinello and Siegwart (2008) presented multidimensional features and shape descriptors that describe geometrical and statistical properties of the segment to detect legs in 2D range data and obtained desirable detection result. Carballo et al (2011) studied laser intensity and textiles, introduced a group of new intensity-based detection features, and proposed a method for segment separation based on laser intensity. They demonstrated that their proposed intensity-based features improved the detection result.…”