A segmentation and data association annotation system for laser-based multi-target tracking evaluation

Abstract

2D laser scanners are now widely used to accomplish robot perception tasks such as SLAM and multi-target tracking (MTT). While a number of SLAM benchmarking datasets are available, only a few works have discussed the issues of collecting multi-target tracking benchmarking datasets. In this work, a segmentation and data association annotation system is proposed for evaluating multi-target tracking using 2D laser scanners. The proposed annotation system uses the existing MTT algorithm to generate initial annotation results and uses camera images as the strong hints to assist annotators to recognize moving objects in laser scans. The annotators can draw the object's shape and future trajectory to automate segmentation and data association and reduce the annotation task loading. The user study results show that the performance of the proposed annotation system is superior in the V-measure vs. annotation speed tests and the false positive and false negative rates.

Detecting negative obstacle using Kinect sensor

“…The 2-D SLAM technique has been used in many indoor robots that have fixed mounted laser range scanner. 2,7 Because of the wide field of view and long measurement distance, this SLAM process can work well in spacious indoor environments and can generate good floor plan map. The limitation of this system is that it cannot detect obstacles that are not in the sensor's field of view, which is in 2-D plane, and cannot detect negative obstacles.…”