Cooperative localization has been proved to effectively outperform single-robot localization. While most of the state-of-the-art multi-robot localization systems either treat moving objects as outliers or accomplish moving object tracking separately from localization, we argue that augmenting moving objects into the localization estimation can further enhance localization performance and is indeed the key to solve several localization challenges such as insufficient map features, no map features, and symmetric maps. In this paper, a multi-robot simultaneous localization and tracking (MR-SLAT) algorithm based on the extended Kalman filter is proposed, and multiple hypothesis tracking (MHT) is integrated into MR-SLAT for dealing with challenging data association issues. The proposed approach is verified in two scenarios: the NAO humanoid robots equipped with cameras and WiFi are used in the RoboCup scenario and the robotic vehicles with laser scanners and dedicated short-range communications (DSRC) are used in the traffic scenario. The experiments with ground truth show that MR-SLAT, by exploiting moving objects, is superior to single-robot localization and cooperative localization in challenging scenarios. Ample experimental and simulation results demonstrate the effectiveness of exploiting moving objects and the generality and feasibility of the proposed MR-SLAT algorithm.

Sensors on mobile phones and wearables, and in general sensors on IoT (Internet of Things), bring forth a couple of new challenges to big data research. First, the power consumption for analyzing sensor data must be low, since most wearables and portable devices are power-strapped. Second, the velocity of analyzing big data on these devices must be high, otherwise the limited local storage may overflow. This paper presents our hardware-software co-design of a classifier for wearables to detect a person's transportation mode (i.e., still, walking, running, biking, and on a vehicle). We particularly focus on addressing the big-data small-footprint requirement by designing a classifier that is low in both computational complexity and memory requirement. Together with a sensor-hub configuration, we are able to drastically reduce power consumption by 99%, while maintaining competitive mode-detection accuracy. The data used in the paper is made publicly available for conducting research.

Localization is one of the most essential capabilities of autonomous robots. Cooperative localization has been proved to be effective in multi-robot localization. However, nearby moving objects could degrade the cooperative localization performance. In this paper, we demonstrate that the cooperative simultaneous localization and tracking approach is superior in challenging scenarios. Localization and moving object tracking are mutually beneficial. The proposed approach is evaluated using humanoid robots in the RoboCup environment in which only uncertain data from onboard cameras and odometry are used. Ample experimental results with ground truthing from laser scanners demonstrate the accuracy and feasibility of the proposed vision-based cooperative simultaneous localization and tracking algorithm.