Amazon Technologies, Inc.

Kespry, Inc.

Chang Young Kim

Roger Robert Webster

Rajasimman Madhivanan

David Allen Fotland

Robert Franklin Ebert

Robert Parker Clark

Nikhil Sharma

Jong Jin Park

James Charles Zamiska

Heidi Christine Schubert

Mohit Deshpande

Madhav Achar

Nathalie Majcherczyk

Madan Jayaprakasam

Rachel Liao

Shashikanth Sreenivasaiah

Stephen Phillips

Maps of a physical space provide information to an autonomous mobile device (AMD) about the location of objects. For example, a local map may represent objects currently detected while a global map represents objects that persist over time. Disagreements between the maps may result from quantization errors, sensor errors, changes in the physical space over time that are shown in one map but not another, and so forth. For example, the local map may indicate an obstacle in a space that the global map indicates is empty, or vice versa. A cluster representing an object in a map may be assessed to distinguish if the object is actual or false. Other techniques may be used to determine disagreements. Information about persistent disagreements may be stored for mitigation. The AMD may mitigate disagreements by exploring the physical space associated with the disagreement to add to map data.

System to determine map data for autonomous mobile device map

An autonomous mobile device (AMD) uses sensors to explore a physical space and determine the locations of obstacles. Simultaneous localization and mapping (SLAM) techniques are used to process images from cameras to determine location and trajectory of the device. As the AMD moves and explores to generate an occupancy map, errors in the localization accrue. The occupancy map is processed to determine a graph that is based on, for a given point in physical space, a distance, relative direction, and a characteristic of a closest obstacle. A first graph based on a global occupancy map may be compared to a second graph based on a local occupancy map to determine a displacement between the local occupancy map and the global occupancy map. To facilitate loop closure, the AMD may navigate to a point along the first graph, where a loop closure process may be performed.

System to facilitate autonomous mobile device movement

An autonomous mobile device (AMD) uses sensors to explore a physical space and determine the locations of obstacles. Simultaneous localization and mapping (SLAM) techniques are used by the AMD to designate as keyframes some images and their associated descriptors of features in the space. Each keyframe indicates a location and orientation of the AMD relative to those features. Anchors are specified relative to keyframes. A marker is specified relative to one or more anchors. Because markers are associated with features in the physical space, they maintain their association with the physical space through various processes such as SLAM loop closures. Markers may specify locations in the physical space, such as navigation waypoints, navigation destinations such as a goal pose for exploring an unexplored area, as an observation target to facilitate exploration, and so forth. Markers may also be used to specify block listed locations to be avoided during exploration.

System for automated exploration by an autonomous mobile device using markers based on image features

An autonomous mobile device (AMD) moves through a physical space without human intervention. Data from sensors on the AMD is used to determine an occupancy map indicative of obstacles and their locations in the physical space. Obstacles may be static, variable, or dynamic. For example, walls are static, doors which open and close are variable, and people moving through the environment are dynamic. Regions in the occupancy map that the AMD has visited but which are shown by the occupancy map as containing obstacles are identified as being variable regions. To determine possible paths through the physical space during autonomous movement, the variable regions are considered to be unoccupied by obstacles and traversable. Information about how often the variable region transitions between traversable and impassible states may also be used to determine the possible paths.

System for autonomous mobile device navigation in dynamic physical space

An autonomous mobile device (AMD) uses sensors to explore a physical space and determine a map indicating the locations of obstacles. As data is later acquired, the map may be updated, resulting in changes to previously mapped obstacle positions. Navigability tests determine if a path can be found between a current location and a previously visited location. A failure to find a path may result in exploration of an area that includes the previously visited location. Submaps may be produced by the AMD as it moves through the physical space. An accuracy score of a selected submap is determined by comparing the selected submap with a partial global map created using submaps other than the selected submap. If the accuracy score is less than a threshold value, the selected submap may be discarded and an area in the physical space associated with the selected submap may be re-explored.

System for assessing an autonomously determined map

An enhanced distance detection system for an autonomous or semi-autonomous vehicle is described here. The distance detection system includes a distance detector, which may have a limited scope of distance detection, and a directional controller, which allows extending the dimension or scope of the distance detector as the vehicle travels and performs missions. The directional controller can change the detection direction of the distance detector with a motorized gimbal or functionally similar system, and the change in the detection direction can be integrated with the status of and other instructions executed by the vehicle.

Enhanced distance detection system

Growing community of inventors

Newark, CA, United States of America

Chang Young Kim