# US 11,307,039 B2 · Combining heterogeneous types of maps

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- Number: US 11,307,039 B2
- Document ID: US11307039B2
- Status: Granted
- Kind: B2
- Application number: 16/439,188
- Filed: 2019-06-12
- Published: 2020-12-17
- Granted: 2022-04-19
- Earliest claimed: 2019-06-12
- Assignees: GM Global Technology Operations LLC
- Applicants: GM Global Technology Operations LLC
- Inventors: Lawrence A. Bush; Michael A. Losh; Aravindhan Mani; Upali P. Mudalige
- Web page: https://patentgrep.com/patent/US11307039B2

## CPC

- G01C 21/32: Structuring or formatting of map data
- G01C 21/3617: {using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement}
- G01C 21/3644: {Landmark guidance, e.g. using POIs or conspicuous other objects}
- G01C: MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY (measuring liquid level G01F; radio navigation, determining distance or velocity by use of propagation effects, e.g. Doppler effects, propagation time, of radio waves, analogous arrangements using other waves G01S)

## Abstract

A system for generating a map includes a processing device configured to determine a provenance of each received map of a plurality of maps, parse each received map into objects of interest, and compare the objects of interest to identify one or more sets of objects representing a common feature. For each set of objects, the processing device is configured to select a subset of the set of objects based on the provenance associated with each object in the set of objects, and calculate a similarity metric for each object in the subset. The similarity metric is selected from an alignment between an object and a reference object in the subset, and/or a positional relationship between the object and the reference object. The processing device is configured to generate a common object representing the common feature based on the similarity metric, and generate a merged map including the common object.

## Claims

### Claim 1

A system for generating a map, comprising:
a processing device including an input module having a processor configured to receive a plurality of maps of a region, the processing device configured to perform:
determining a provenance of each received map of the plurality of maps, the provenance determined based on at least one of a source and a resolution of each received map;
parsing each received map into objects of interest representing features of interest in the region, wherein the objects of interest include one or more lane objects, each one or more lane object representing a segment of a road lane;
comparing the objects of interest from the plurality of maps to identify one or more sets of objects, each set of objects representing a common feature;
for each set of objects, performing:
selecting a subset of the set of objects based on a provenance rank associated with each object in the set of objects, the provenance rank of an object based on the resolution of a received map that includes the object;
selecting an object from the subset as a reference object, and estimating a distance from the reference object to each other object in the subset, wherein the reference object is selected based on the provenance associated with the reference object and a length of the reference object;
calculating a similarity metric for each object in the subset, the similarity metric selected from at least one of: an alignment between an object in the subset and the reference object in the subset, and a positional relationship between the object in the subset and the reference object in the subset;
generating a common object representing the common feature from at least one object in the subset based on the similarity metric; and
generating a merged map including the common object.

### Claim 2 (depends on 1)

The system of claim 1, wherein the processing device is configured to estimate a length of each object in the subset, and merge objects having a length that is shorter than a selected length.

### Claim 3 (depends on 1)

The system of claim 1, wherein the processing device is configured to generate the common object based on calculating a weighted average of a lateral position of each object relative to the reference object.

### Claim 4 (depends on 3)

The system of claim 3, wherein calculating the weighted average includes assigning a weight to each object in the subset based on the provenance.

### Claim 5 (depends on 1)

The system of claim 1, wherein the similarity metric for each object in the subset is an alignment metric based on at least one of: a projection of each object onto the reference object, and an angular heading of each object relative to the reference object.

### Claim 6 (depends on 1)

The system of claim 1, wherein the processing device is part of a vehicle or in communication with the vehicle.

### Claim 7

A method for generating a map, comprising:
receiving a plurality of maps of a region by a processing device;
determining a provenance of each received map of the plurality of maps, the provenance determined based on at least one of a source and a resolution of each received map;
parsing each received map into objects of interest representing features of interest in the region, wherein the objects of interest include one or more lane objects, each one or more lane object representing a segment of a road lane;
comparing the objects of interest from the plurality of maps to identify one or more sets of objects, each set of objects representing a common feature;
for each set of objects, performing:
selecting a subset of the set of objects based on a provenance rank associated with each object in the set of objects, the provenance rank of an object based on the resolution of a received map that includes the object;
selecting an object from the subset as a reference object, and estimating a distance from the reference object to each other object in the subset, wherein the reference object is selected based on the provenance associated with the reference object and a length of the reference object;
calculating a similarity metric for each object in the subset, the similarity metric selected from at least one of: an alignment between an object in the subset and the reference object in the subset, and a positional relationship between the object in the subset and the reference object in the subset;
generating a common object representing the common feature from at least one object in the subset based on the similarity metric; and
generating a merged map including the common object.

### Claim 8 (depends on 7)

The method of claim 7, wherein generating the common object includes estimating a length of each object in the subset, and merging objects having a length that is shorter than a selected length.

### Claim 9 (depends on 7)

The method of claim 7, wherein the common object is generated based on calculating a weighted average of a lateral position of each object relative to the reference object.

### Claim 10 (depends on 9)

The method of claim 9, wherein calculating the weighted average includes assigning a weight to each object in the subset based on the provenance.

### Claim 11 (depends on 7)

The method of claim 7, wherein the similarity metric for each object in the subset is an alignment metric based on at least one of: a projection of each object onto the reference object, and an angular heading of each object relative to the reference object.

### Claim 12 (depends on 7)

The method of claim 7, wherein the processing device is part of a vehicle or in communication with the vehicle.