Affordable SLAM through the co-design of hardware and methodology

Abstract

Abstract-Simultaneous localization and mapping (SLAM) is a prominent feature for autonomous robots operating in undefined environments. Applications areas such as consumer robotics appliances would clearly benefit from low-cost and compact SLAM implementations. The SLAM research community has developed several robust algorithms in the course of the last two decades. However, until now most SLAM demonstrators have relied on expensive sensors or large processing power, limiting their realms of application. Several works have explored optimizations into various directions; however none has presented a global optimization from the mechatronic to the algorithmic level.In this article, we present a solution to the SLAM problem based on the co-design of a slim rotating distance scanner, a lightweight SLAM software, and an optimization methodology. The scanner consists of a set of infrared distance sensors mounted on a contactless rotating platform. The SLAM algorithm is an adaptation of FastSLAM 2.0 that runs in real time on a miniature robot. The optimization methodology finds the parameters of the SLAM algorithm using an evolution strategy.This work demonstrates that an inexpensive sensor coupled with a low-speed processor are good enough to perform SLAM in simple environments in real time.

Socially-Mediated Negotiation for Obstacle Avoidance in Collective Transport

“…Each of the three simulated mobile robots is equipped with a number of sensors and actuators. We considered and used only the following sensors and actuators: i) a light sensor, that is able to perceive the intensity of the light coming from different directions around the robot; ii) a distance scanner, that is used to obtain distance and angular values from the robot to other objects in the environment [17]; iii) a range and bearing communication system, with which a robot can send a message to other nearby robots in line of sight [18]; iv) a gripper, that is used to physically connect to the transported robot considered in the experiment; v) a turret actuator which, when set to active mode, can be used to rotate the gripper installed on a rotating ring or, when set to passive mode, can freely rotate in accordance with the speed of the wheels when the gripper is gripping an heavy object; vi) a wheels actuator, that is used to control independently the speed of the left and right wheels of the robot. The light sensor and the distance scanner sensor are not perfect but subject to a certain degree of noise.…”

The Autonomous Photovoltaic MarXbot

“…Subsystems can be put into sleep when unused, and we are thus able to minimise the wasted energy when recharging. Its rich set of sensors provides the robot with advanced navigation capabilities, like a low-cost range scanner and an embedded ARM-based computer [9] (Fig. 1a).…”

Real time simultaneous localization and mapping: towards low-cost multiprocessor embedded systems

“…Authors have underlined the difficulty to find the right SLAM parameters to fit within the available computing power and the real-time processing. Magnenat et al [12] present a system based on the co-design of a low-cost sensor (a slim rotating scanner), a SLAM algorithm, a computing unit, and an optimization methodology. The computing unit is based on an ARM processor (533 Mhz) running a FASTSLAM 2.0 algorithm [13].…”

“…The computing unit is based on an ARM processor (533 Mhz) running a FASTSLAM 2.0 algorithm [13]. Magnenat et al [12] use an evolution strategy to find the best configuration of the algorithm and setting of the parameters.…”

“…As pointed out by [11,12], the first improvement of a SLAM algorithm is an efficient setting of the various parameters of the algorithm. Other modifications were investigated to reach real-time constraints.…”