A robot understands its world through the raw information it senses from its surroundings. This raw information is not suitable as a shared representation between the robot and its user. A semantic map, containing high-level information that both the robot and user understand, is better suited to be a shared representation. We use the semantic map as the user-facing interface on our fleet of floor-cleaning robots. Jitter in the robot's sensed raw map, dynamic objects in the environment, and exploration of new space by the robot are common challenges for robots. Solving these challenges effectively in the context of semantic maps is key to enabling semantic maps for lifelong mapping. First, as a robot senses new changes and alters its raw map in successive runs, the semantics must be updated appropriately. We update the map using a spatial transfer of semantics. Second, it is important to keep semantics and their relative constraints consistent even in the presence of dynamic objects. Inconsistencies are automatically determined and resolved through the introduction of a map layer of meta-semantics. Finally, a discovery phase allows the semantic map to be updated with new semantics whenever the robot uncovers new information. Deployed commercially on thousands of floor-cleaning robots in real homes, our user-facing semantic maps provide a intuitive user experience through a lifelong mapping robot.

Occupancy mapping is an important component of a robot navigation system since it enables intelligent planning of actions to accomplish a task. In some cases, occupancy information is mapped as part of a SLAM system. In other cases, such as visual SLAM, an occupancy map must be separately created, harnessing localization information. We present a novel occupancy mapping method that adaptively and efficiently takes advantage of improved trajectory estimates from the localization system, without requiring storage of all poses of the trajectory or the sensor information from all poses. Occupancy information is stored in local maps anchored to poses in the trajectory. Local maps are created adaptively depending on the uncertainty of localization. The local maps maintain consistency of occupancy information independently of changes in the localization map due to loop closures or convergence of the estimation algorithm. At any time, a global occupancy map (snapshot map) can be rendered from the local occupancy maps. We evaluate the performance of the system using real data and compare it to two baseline methods.

Localization using continuous signals such as WiFi or active beacons is a cost-effective approach for enabling systematic navigation of robots. In our previous work we showed how localization maps, represented as regular grids, of such signals can be learned through application of vector field SLAM [1]. In this paper we describe a method that, given such a localization map, finds the pose of a mobile robot from observations of the signals. Our method first generates pose hypotheses by searching the localization map for places that best fit to a measurement taken by the robot. A localization filter using an extended Kalman filter (EKF) then verifies one pose hypothesis by tracking the pose over a short distance. In experiments carried out in a standard test environment equipped with active beacons we obtain an average position accuracy of 10 to 35 cm with a localization success rate of 96 to 99 %. The proposed method enables a robot mapping an environment using vector field SLAM to recover from kidnapping and resume its navigation.

Localization in unknown environments using low-cost sensors on embedded hardware is challenging. Yet, it is a requirement for consumer robots if systematic navigation is desired. In this paper, we present a localization approach that learns the spatial variation of an observed continuous signal over the environment. We model the signal as a piecewise linear function and estimate its parameters using a simultaneous localization and mapping (SLAM) approach. By applying the concepts of the exactly sparse extended information filter (ESEIF) , a constant-time, linear-space algorithm is obtained under certain approximations. We apply our framework to a sensor measuring bearing to active beacons, where measurements are distorted because of occlusion and signal reflections. Experimental results from running GraphSLAM, extended Kalman filter SLAM, and ESEIF-SLAM on manually collected sensor measurements, as well as on data recorded on a vacuum-cleaner robot, validate our model. The ESEIF-SLAM solution is evaluated on an ARM 7 embedded board with 64-kB RAM connected to a Roomba 510 vacuum cleaner. The presented methods are also used in Evolution Robotics ' Mint Cleaner product for autonomous floor cleaning.

Localization in unknown environments using low-cost sensors remains a challenge. This paper presents a new localization approach that learns the spatial variation of an observed continuous signal. We model the signal as a piece-wise linear function and estimate its parameters using a simultaneous localization and mapping (SLAM) approach. We apply our framework to a sensor measuring bearing to active beacons where measurements are systematically distorted due to occlusion and signal reflections of walls and other objects present in the environment. Experimental results from running GraphSLAM and EKF-SLAM on manually collected sensor measurements as well as on data recorded on a vacuum-cleaner robot validate our model.

We present a graph-based SLAM approach, using monocular vision and odometry, designed to operate on computationally constrained platforms. When computation and memory are limited, visual tracking becomes difficult or impossible, and map representation and update costs must remain low. Our system constructs a map of structured views using only weak temporal assumptions, and performs recognition and relative pose estimation over the set of views. Visual observations are fused with differential sensors in an incrementally optimized graph representation. Using variable elimination and constraint pruning, the graph complexity and storage is kept linear in explored space rather than in time. We evaluate performance on sequences with ground truth, and also compare to a standard graph SLAM approach.

In applications like motion capture, high speed collision testing and robotic manipulation of deformable objects there is a critical need for capturing the 3D geometry of fast moving and/or deforming objects. Although there exists many 3D sensing techniques, most cannot deal with dynamic scenes (e.g., laser scanning). Others, like stereovision, require that object surfaces be appropriately textured. Few, if any, build high-resolution 3D models of dynamic scenes. This paper presents a technique to compute high-resolution range maps from single images of moving and deforming objects. This method is based on observing the deformation of a projected light pattern that combines a set of parallel colored stripes and a perpendicular set of sinusoidal intensity stripes. While the colored stripes allow the sensor to compute absolute depths at coarse resolution, the sinusoidal intensity stripes give dense relative depths. This twofold pattern makes it possible to extract a high-resolution range map from each image in a video sequence. The sensor has been implemented and tested on several deforming objects.