Human detection and tracking is an essential task for service robots, where the combined use of multiple sensors has potential advantages that are yet to be exploited. In this paper, we introduce a framework allowing a robot to learn a new 3D LiDAR-based human classifier from other sensors over time, taking advantage of a multisensor tracking system. The main innovation is the use of different detectors for existing sensors (i.e. RGB-D camera, 2D LiDAR) to train, online, a new 3D LiDAR-based human classifier, exploiting a so-called trajectory probability. Our framework uses this probability to check whether new detections belongs to a human trajectory, estimated by different sensors and or detectors, and to learn a human classifier in a semi-supervised fashion. The framework has been implemented and tested on a real-world dataset collected by a mobile robot. We present experiments illustrating that our system is able to effectively learn from different sensors and from the environment, and that the performance of the 3D LiDAR-based human classification improves with the number of sensors detectors used.
@cite_13: This paper presents a novel approach to detect and track people and cars based on the combined information retrieved from a camera and a laser range scanner. Laser data points are classified by using boosted Conditional Random Fields, while the image based detector uses an extension of the Implicit Shape Model (ISM), which learns a codebook of local descriptors from a set of hand-labeled images and uses them to vote for centers of detected objects. Our extensions to ISM include the learning of object parts and template masks to obtain more distinctive votes for the particular object classes. The detections from both sensors are then fused and the objects are tracked using a Kalman Filter with multiple motion models. Experiments conducted in real-world urban scenarios demonstrate the effectiveness of our approach.
@cite_22: 
@cite_2: Tracking people is a key technology for robots and intelligent systems in human environments. Many person detectors, filtering methods and data association algorithms for people tracking have been proposed in the past 15+ years in both the robotics and computer vision communities, achieving decent tracking performances from static and mobile platforms in real-world scenarios. However, little effort has been made to compare these methods, analyze their performance using different sensory modalities and study their impact on different performance metrics. In this paper, we propose a fully integrated real-time multi-modal laser RGB-D people tracking framework for moving platforms in environments like a busy airport terminal. We conduct experiments on two challenging new datasets collected from a first-person perspective, one of them containing very dense crowds of people with up to 30 individuals within close range at the same time. We consider four different, recently proposed tracking methods and study their impact on seven different performance metrics, in both single and multi-modal settings. We extensively discuss our findings, which indicate that more complex data association methods may not always be the better choice, and derive possible future research directions.
RGB RGB-D camera plus 2D LiDAR is the most frequently used combination in the literature. @cite_22 presented two different methods for mobile robot tracking and following of a fast-moving person in an outdoor environment. The robot was equipped with an omnidirectional camera and a 2D LiDAR. @cite_13 presented an integrated system to detect and track people and cars in outdoor scenarios, based on the information retrieved from a camera and a 2D LiDAR on an autonomous car. @cite_2 introduced a people tracking system for mobile robots in very crowded and dynamic environments. Their system was evaluated with a robot equipped with two RGB-D cameras, a stereo camera and two 2D LiDARs.