# US 2026/0267002 A1 · Systems and methods for a portable robot with collapsible tower and sensor protection

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- Number: US 2026/0267002 A1
- Document ID: US20260267002A1
- Status: Published application
- Kind: A1
- Application number: 19/558,185
- Filed: 2026-03-05
- Published: 2026-09-10
- Earliest claimed: 2025-03-07
- Assignees: Brain Corporation
- Applicants: Brain Corporation
- Inventors: Joseph Cognato; Daniel Vandewiele; Matthew Atlas; Joe Fernando Nunez; Jeremiah Cox
- Web page: https://patentgrep.com/patent/US20260267002A1

## CPC

- G01S 17/894: Three-dimensional [3D] imaging with simultaneous measurement of time-of-flight at a two-dimensional [2D] array of receiver pixels, e.g. time-of-flight cameras or flash lidar
- G01S 7/4813: {Housing arrangements}
- G01S 7/4972: {Alignment of sensor}
- G01S 17/86: Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
- G06T 7/33: using feature-based methods
- G06T 2207/20084: Artificial neural networks [ANN]
- G06T 2207/20212: Image combination
- G01S: RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

A robotic system for indoor mapping includes a base with a differential drive and a collapsible tower comprising a plurality of tower sections foldable at multiple joints. A folding mechanism transitions the tower between an extended configuration and a collapsed configuration in which the tower lies substantially flush with the base. A plurality of cameras are mounted along the tower and vertically aligned with a wheel axis of the differential drive such that the cameras rotate in place without lateral translation when the base turns, reducing image distortion. A 3D LiDAR sensor having a dome-like shape protrudes from the tower and is received within a protective cavity formed in an adjacent tower section when the tower is collapsed. A processor executes instructions to obtain image data and range data for mapping, navigation, and self-calibration.

## Claims

### Claim 1

A robotic system for indoor mapping, comprising:
a base comprising a differential drive having a wheel axis;
a tower coupled to the base and comprising a plurality of tower sections including a lower section and at least one upper section, the tower being collapsible at a plurality of joints between adjacent tower sections;
a folding mechanism configured to fold the tower from an extended configuration into a collapsed configuration in which the tower lies substantially flush with a top of the base;
a plurality of cameras mounted to the tower along a line that is vertically aligned with the wheel axis, the plurality of cameras being oriented substantially perpendicular to a direction of travel of the robotic system such that rotation of the base about the wheel axis causes the plurality of cameras to rotate in place without substantial lateral translation of corresponding fields of view;
a three-dimensional (3D) LiDAR sensor mounted to the robotic system;
a memory comprising computer readable instructions stored thereon; and
at least one processor configured to execute the computer readable instructions to cause the robotic system to:
obtain image data from the plurality of cameras while the robotic system traverses a path adjacent to one or more objects; and
obtain range data from the 3D LiDAR sensor while the robotic system traverses the path.

### Claim 2 (depends on 1)

The robotic system of claim 1, further comprising:
a cavity is formed in one of the tower sections, and
wherein,
the 3D LiDAR sensor is mounted to a front-facing side of the tower and has a dome-like shape that protrudes outward from the tower; and
the cavity is positioned such that, in the collapsed configuration, the cavity at least partially receives the dome-like shape of the 3D LiDAR sensor to protect the 3D LiDAR sensor during transportation and storage.

### Claim 3 (depends on 1)

The robotic system of claim 1, wherein the instructions further cause the at least one processor to generate an orthographic composite image of the one or more objects by concatenating pixel regions extracted from a plurality of captured images, each pixel region corresponding to a central band of an image captured at substantially normal incidence.

### Claim 4 (depends on 1)

The robotic system of claim 1, wherein the instructions further cause the at least one processor to:
identify keypoints in overlapping regions of the image data captured by at least one of the plurality of cameras;
compute a geometric transformation based on the keypoints; and
generate a panoramic image of the one or more objects by aligning and blending the image data according to the geometric transformation.

### Claim 5 (depends on 1)

The robotic system of claim 1, wherein the instructions further cause the at least one processor to:
cause the plurality of cameras to capture an image of at least one object at a current distance;
determine a focus metric of the image using a Laplacian-based variance calculation or a trained machine learning model;
compare the focus metric to a focus threshold; and
in response to the focus metric being below the focus threshold, adjust a navigating distance between the robotic system and the at least one object and cause the plurality of cameras to recapture the image.

### Claim 6 (depends on 1)

The robotic system of claim 1, wherein:
the memory stores reference range measurements associated with at least one known static portion of the robotic system; and
the instructions further cause the at least one processor to:
obtain range data from the 3D LiDAR sensor including range measurements to the at least one known static portion of the robotic system;
compare the range measurements to the reference range measurements;
determine, based on the comparison, whether the 3D LiDAR sensor is misaligned relative to the base; and
in response to determining that the 3D LiDAR sensor is misaligned, apply a corrective transformation to subsequent range data or generate a diagnostic alert indicating a calibration condition.

### Claim 7

A method for operating a robotic system for indoor mapping, the method comprising:
providing a robotic system comprising:
a base comprising a differential drive having a wheel axis,
a tower coupled to the base and comprising a plurality of tower sections including a lower section and at least one upper section, the tower being collapsible at a plurality of joints between adjacent tower sections,
a folding mechanism configured to fold the tower from an extended configuration into a collapsed configuration in which the tower lies substantially flush with a top of the base,
a plurality of cameras mounted to the tower along a line that is vertically aligned with the wheel axis, the plurality of cameras being oriented substantially perpendicular to a direction of travel of the robotic system such that rotation of the base about the wheel axis causes the plurality of cameras to rotate in place without substantial lateral translation of corresponding fields of view,
a three-dimensional (3D) LiDAR sensor mounted to the robotic system, and
a memory comprising computer readable instructions stored thereon;
navigating the robotic system along a path adjacent to one or more objects while the tower is in the extended configuration;
obtaining, by the plurality of cameras, image data of the one or more objects while the robotic system traverses the path; and
obtaining, by the 3D LiDAR sensor, range data while the robotic system traverses the path.

### Claim 8 (depends on 7)

The method of claim 7, wherein the robotic system further comprises:
a cavity formed in one of the tower sections, and
wherein,
the 3D LiDAR sensor is mounted to a front-facing side of the tower and has a dome-like shape that protrudes outward from the tower; and
the cavity is positioned such that, in the collapsed configuration, the cavity at least partially receives the dome-like shape of the 3D LiDAR sensor to protect the 3D LiDAR sensor during transportation and storage.

### Claim 9 (depends on 7)

The method of claim 7, further comprising:
generating, by at least one processor executing the computer readable instructions, an orthographic composite image of the one or more objects by concatenating pixel regions extracted from a plurality of captured images, each pixel region corresponding to a central band of an image captured at substantially normal incidence.

### Claim 10 (depends on 7)

The method of claim 7, further comprising, by at least one processor executing the computer readable instructions,
identifying keypoints in overlapping regions of the image data captured by at least one of the plurality of cameras;
computing a geometric transformation based on the keypoints; and
generating a panoramic image of the one or more objects by aligning and blending the image data according to the geometric transformation.

### Claim 11 (depends on 7)

The method of claim 7, further comprising, by at least one processor executing the computer readable instructions:
causing the plurality of cameras to capture an image of at least one object at a current distance;
determining a focus metric of the image using a Laplacian-based variance calculation or a trained machine learning model;
comparing the focus metric to a focus threshold; and
in response to the focus metric being below the focus threshold, adjusting a navigating distance between the robotic system and the at least one object and causing the plurality of cameras to recapture the image.

### Claim 12 (depends on 7)

The method of claim 7, wherein,
the memory stores reference range measurements associated with at least one known static portion of the robotic system; and
the method further comprises, by at least one processor executing the computer readable instructions:
obtaining range data from the 3D LiDAR sensor including range measurements to the at least one known static portion of the robotic system;
comparing the range measurements to the reference range measurements;
determining, based on the comparison, whether the 3D LiDAR sensor is misaligned relative to the base; and
in response to determining that the 3D LiDAR sensor is misaligned, applying a corrective transformation to subsequent range data or generating a diagnostic alert indicating a calibration condition.

### Claim 13 (depends on 7)

The method of claim 7, further comprising folding the tower from the extended configuration into the collapsed configuration using the folding mechanism, wherein folding the tower causes the tower to become flush with the top of the base.

### Claim 14 (depends on 13)

The method of claim 13, further comprising protecting the 3D LiDAR sensor during transportation and storage, wherein folding the tower causes a cavity formed in one of the tower sections to automatically move over at least a portion of the 3D LiDAR sensor.