A sensor is a device that converts a physical parameter or an environmental characteristic (e.g., temperature, distance, speed, etc.) into a signal that can be digitally measured and processed to perform specific tasks. Mobile robots need sensors to measure properties of their environment, thus allowing for safe navigation, complex perception and corresponding actions, and effective interactions with other agents that populate it. Sensors used by mobile robots range from simple tactile sensors, such as bumpers, to complex vision-based sensors such as structured light RGB-D cameras. All of them provide a digital output (e.g., a string, a set of values, a matrix, etc.) that can be processed by the robot's computer. Such output is typically obtained by discretizing one or more analog electrical signals by using an Analog to Digital Converter (ADC) included in the sensor. In this chapter we present the most common sensors used in mobile robotics, providing an introduction to their taxonomy, basic features, and specifications. The description of the functionalities and the types of applications follows a bottom-up approach: the basic principles and components on which the sensors are based are presented before describing real-world sensors, which are generally based on multiple technologies and basic devices.

This paper presents a data driven approach to multi-modal fusion, where optimal features for each sensor are selected from a common hidden space between the different modalities. The existence of such a hidden space is then used in order to detect damaged sensors and safeguard the performance of the system. Experimental results show that such an approach can make the system robust against noisy/damaged sensors, without requiring human intervention to inform the system about the damage.

Sony, the global leader in imaging sensors -- both for smartphones and professional DSLR and mirrorless cameras -- is eager to establish itself as the go-to supplier for the next generation of visual-processing chips with a set of new 3D sensors. Speaking with Bloomberg last week, Sony's sensor division boss Satoshi Yoshihara said Sony plans to ramp up production of chips to power front and rear 3D cameras in late summer, responding to demand from multiple smartphone manufacturers. Though Yoshihara is geeked about the potential for augmented reality applications, the most intriguing aspect of this new tech would appear to be a better form of face identification than we currently have. The Face ID approach that Apple first brought into use on the iPhone X -- and others like Xiaomi, Huawei, and Vivo have since emulated -- works by projecting out a grid of invisible dots and detecting the user's face by the deformations of that grid in 3D space. Sony's 3D sensor, on the hand, is said to deploy laser pulses, which, much like a bat's echolocation, creates a depth map of its surroundings by measuring how long a pulse takes to bounce back.

From body parts to supercars, the family of 3D printed products just keeps expanding. But in a study published last week in Science Advances, scientists think small: German researchers 3D printed different lenses--each smaller than the width of a human hair--onto a chip. Such micro-cameras could be perfect for tiny drones and other pint-sized robots. "Our system is the only one in the world [where] you can put different optic systems on one imaging sensor that is very small," says study author Alois Herkommer, an applied physicist at the University of Stuttgart in Stuttgart, Germany. "The advantage of doing this by 3D printing is that each of these lenses can be different," says Herkommer.