Abstract: Embodiments are provided that include receiving sensor data from a sensor positioned at a plurality of positions in an environment. The environment includes a plurality of landmarks. The embodiments also include determining, based on the sensor data, a subset of the plurality of landmarks detected at each of the plurality of positions. The embodiments further include determining, based on the subset of the plurality of landmarks detected at each of the plurality of positions, a detection frequency of each landmark. The embodiments additionally include determining, based on the determined detection frequency of each landmark, a localization viability metric associated with each landmark. The embodiments still further include providing for display, via a user interface, a map of the environment. The map includes an indication of the localization viability metric associated with each landmark.

Abstract: Examples relate to simultaneous localization and calibration. An example implementation may involve receiving sensor data indicative of markers detected by a sensor on a vehicle located at vehicle poses within an environment, and determining a pose graph representing the vehicle poses and the markers. For instance, the pose graph may include edges associated with a cost function representing a distance measurement between matching marker detections at different vehicle poses. The distance measurement may incorporate the different vehicle poses and a sensor pose on the vehicle. The implementation may further involve determining a sensor pose transform representing the sensor pose on the vehicle that optimizes the cost function associated with the edges in the pose graph, and providing the sensor pose transform. In further examples, motion model parameters of the vehicle may be optimized as part of a graph-based system as well or instead of sensor calibration.

Abstract: Methods and systems for detecting sensor orientation characteristics using marker-based localization are disclosed herein. In one aspect, a robotic device can: receive a map of a horizontal marker plane that includes mapped positions of a first marker and a second marker arranged in the horizontal marker plane; receive, from a sensor configured to scan a two-dimensional sensor plane, sensor data indicative of positions of the first and second markers relative to the sensor; determine measured positions of the first and second markers based on the sensor data and a current position of the sensor; determine a difference vector between a first vector that connects the mapped positions of the first and second markers and a second vector that connects the measured positions of the first and second markers; and determine, based on the difference vector, an orientation of the two-dimensional sensor plane relative to the horizontal marker plane.

Abstract: Embodiments are provided that include maintaining a map of a plurality of markers in an environment. The map includes a last detection time of each marker of the plurality of markers. The embodiments also include receiving a set of detected markers from a robotic device that is configured to localize in the environment using the plurality of markers. The embodiments further include updating, in the map, the last detection time of each marker which has a mapped position that corresponds to a detected position of a detected marker in the set of detected markers. The embodiments additionally include identifying, from the plurality of markers in the map, a marker having a last detection time older than a threshold amount of time. The embodiments still further include initiating an action related to the identified marker.

Abstract: One method disclosed includes identifying, in a map of markers fixed in an environment, two co-located markers within a threshold distance of each other, where each of the two co-located markers has a non-overlapping visibility region. The method further includes determining a set of detected markers based on sensor data from a robotic device. The method additionally includes identifying, from the set of detected markers, a detected marker proximate to a first marker of the two co-located markers. The method also includes enforcing a visibility constraint based on the non-overlapping visibility region of each of the two co-located markers to determine an association between the detected marker and a second marker of the two co-located markers. The method further includes determining a location of the robotic device in the environment relative to the map based on the determined association.

Abstract: One method disclosed includes determining a map of markers fixed in an environment, where the map of markers includes a location and an orientation of each marker. The method further includes determining locations of a set of detected markers relative to the map based on a location of a robotic device relative to the map and based on sensor data from the robotic device. The method also includes associating a detected marker from the set of detected markers with a mapped marker based on the determined location of the detected marker relative to the map and based on a visibility constraint related to the orientation of the mapped marker. The method additionally includes adjusting, in the map, the orientation of the mapped marker based on the determined location of the detected marker relative to the map.

Abstract: Example implementations may relate methods and systems for detecting, recognizing, and localizing pallets. For instance, a computing system may receive sensor data representing aspects of an environment, and identify a set of edge points in the sensor data. The computing system may further determine a set of line segments from the set of edge points where each line segment may fit to a subset of the set of edge points. Additionally, the computing system may also filter the set of line segments to exclude line segments that have a length outside a height range and a width range associated with dimensions of a pallet template, and identify, from the filtered set of line segments, a subset of line segments that align with the pallet template. Based on the identified subset of line segments, the computing system may determine a pose of a pallet in the environment.

Abstract: Methods and systems for detecting sensor orientation characteristics using marker-based localization are disclosed herein. In one aspect, a robotic device can: receive a map of a horizontal marker plane that includes mapped positions of a first marker and a second marker arranged in the horizontal marker plane; receive, from a sensor configured to scan a two-dimensional sensor plane, sensor data indicative of positions of the first and second markers relative to the sensor; determine measured positions of the first and second markers based on the sensor data and a current position of the sensor; determine a difference vector between a first vector that connects the mapped positions of the first and second markers and a second vector that connects the measured positions of the first and second markers; and determine, based on the difference vector, an orientation of the two-dimensional sensor plane relative to the horizontal marker plane.

Abstract: Example implementations relate to map generation and alignment. For instance, a computing system may receive and use sensor data indicative of positions of multiple markers positioned relative to a sensor within an environment to determine a pose of the sensor and also create a map that indicates the markers positions. The computing system may also receive and use subsequent sensor data indicative of distances from the sensor to surfaces in the environment and the determined pose of the sensor to determine an occupancy grid map that represents the surfaces within the environment. The computing system may then determine a transformation between the map of the markers and a design model of the environment that relates occupied cells in the occupancy grid map to sampled points from the design model, and provide the transformation between the map of the plurality of markers and the design model.

Abstract: Methods and systems for detecting sensor orientation characteristics using marker-based localization are disclosed herein. In one aspect, a robotic device can: receive a map of a horizontal marker plane that includes mapped positions of a first marker and a second marker arranged in the horizontal marker plane; receive, from a sensor configured to scan a two-dimensional sensor plane, sensor data indicative of positions of the first and second markers relative to the sensor; determine measured positions of the first and second markers based on the sensor data and a current position of the sensor; determine a difference vector between a first vector that connects the mapped positions of the first and second markers and a second vector that connects the measured positions of the first and second markers; and determine, based on the difference vector, an orientation of the two-dimensional sensor plane relative to the horizontal marker plane.

Abstract: Embodiments are provided that include receiving sensor data from a sensor positioned at a plurality of positions in an environment. The environment includes a plurality of landmarks. The embodiments also include determining, based on the sensor data, a subset of the plurality of landmarks detected at each of the plurality of positions. The embodiments further include determining, based on the subset of the plurality of landmarks detected at each of the plurality of positions, a detection frequency of each landmark. The embodiments additionally include determining, based on the determined detection frequency of each landmark, a localization viability metric associated with each landmark. The embodiments still further include providing for display, via a user interface, a map of the environment. The map includes an indication of the localization viability metric associated with each landmark.

Abstract: Example implementations relate to map generation and alignment. For instance, a computing system may receive and use sensor data indicative of positions of multiple markers positioned relative to a sensor within an environment to determine a pose of the sensor and also create a map that indicates the markers positions. The computing system may also receive and use subsequent sensor data indicative of distances from the sensor to surfaces in the environment and the determined pose of the sensor to determine an occupancy grid map that represents the surfaces within the environment. The computing system may then determine a transformation between the map of the markers and a design model of the environment that relates occupied cells in the occupancy grid map to sampled points from the design model, and provide the transformation between the map of the plurality of markers and the design model.

Abstract: Example embodiments include determining a map of an environment of a robotic vehicle. The map includes locations of a plurality of mapped landmarks within the environment and a false detection source region within the environment. The embodiments further include detecting a plurality of candidate landmarks, and determining which of the detected candidate landmarks correspond to one of the plurality of mapped landmarks and which correspond to false detections. The embodiments additionally include estimating a pose of the robotic vehicle within the environment. The embodiments further include determining which of the detected candidate landmarks determined to correspond to false detections fall within the false detection source region. The embodiments still further include determining a confidence level of the pose estimate based on which of the detected candidate landmarks determined to correspond to false detections fall within the false detection source region.

Abstract: Example implementations may relate methods and systems for detecting, recognizing, and localizing pallets. For instance, a computing system may receive sensor data representing aspects of an environment, and identify a set of edge points in the sensor data. The computing system may further determine a set of line segments from the set of edge points where each line segment may fit to a subset of the set of edge points. Additionally, the computing system may also filter the set of line segments to exclude line segments that have a length outside a height range and a width range associated with dimensions of a pallet template, and identify, from the filtered set of line segments, a subset of line segments that align with the pallet template. Based on the identified subset of line segments, the computing system may determine a pose of a pallet in the environment.

Abstract: Examples relate to simultaneous localization and calibration. An example implementation may involve receiving sensor data indicative of markers detected by a sensor on a vehicle located at vehicle poses within an environment, and determining a pose graph representing the vehicle poses and the markers. For instance, the pose graph may include edges associated with a cost function representing a distance measurement between matching marker detections at different vehicle poses. The distance measurement may incorporate the different vehicle poses and a sensor pose on the vehicle. The implementation may further involve determining a sensor pose transform representing the sensor pose on the vehicle that optimizes the cost function associated with the edges in the pose graph, and providing the sensor pose transform. In further examples, motion model parameters of the vehicle may be optimized as part of a graph-based system as well or instead of sensor calibration.

Abstract: An example method includes determining locations of a plurality of candidate landmarks in relation to a robot based on sensor data from at least one sensor on the robot. The method further includes determining a plurality of sample sets, wherein each sample set comprises a subset of the plurality of candidate landmarks and a plurality of corresponding mapped landmarks. The method also includes determining a transformation for each sample set that relates the candidate landmarks from the subset to the corresponding mapped landmarks. The method additionally includes applying the determined transformation for each sample set to the plurality of candidate landmarks to determine a number of inliers associated with each sample set based on distances between the transformed plurality of candidate landmarks and a plurality of neighbouring mapped landmarks. The method further includes selecting a sample set from the plurality based on the number of inliers associated with each sample set.

Abstract: Methods and systems for detecting sensor orientation characteristics using marker-based localization are disclosed herein. In one aspect, a robotic device can: receive a map of a horizontal marker plane that includes mapped positions of a first marker and a second marker arranged in the horizontal marker plane; receive, from a sensor configured to scan a two-dimensional sensor plane, sensor data indicative of positions of the first and second markers relative to the sensor; determine measured positions of the first and second markers based on the sensor data and a current position of the sensor; determine a difference vector between a first vector that connects the mapped positions of the first and second markers and a second vector that connects the measured positions of the first and second markers; and determine, based on the difference vector, an orientation of the two-dimensional sensor plane relative to the horizontal marker plane.