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Jigsaw: Indoor Floor Plan Reconstruction

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Thomas Anderson

on 3 November 2014

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Transcript of Jigsaw: Indoor Floor Plan Reconstruction

Jigsaw: Description
A floor plan reconstruction system that
leverages crowdsensed data from mobile users.


Challenges
Accurate coordinates and orientations of indoor landmarks.
Insufficient “anchor points”.

Design overview
Paper Details
EECS School, Peking University, China
Ruipeng Gao, Mingmin Zhao, Tao Ye,
Yizhou Wang, Kaigui Bian, Tao Wang,
Xiaoming Li

ECE Dept., Stony Brook University
Fan Ye

MobiCom’14, September 7-11, 2014
Motivation
Lack of indoor floor plans Indoor localization sporadic.
Jigsaw: Indoor Floor Plan Reconstruction
via Mobile Crowdsensing

Effort - intensive and time - consuming
Need to hire dedicated personnel.

Inspiration
Complementary strengths of vision and mobile techniques
Use optimization and probabilistic formulations.
Landmark
modeling
Map
augmentation
Landmark
placement
1. Landmark modeling
Extract sizes and coordinates of major geometry features of POI

Coordinates of
Geometric Vertices
1. Extract landmark’s major contour lines
Connecting points of
Wall Segments
3. Detect connecting points of wall segments.
Landmark Model
Set of Images
2. Project 2D lines into 3D
Structure from Motion (SfM)
Generates (in the LOCAL coordinate system) :
• a “cloud” of 3d points representing the exterior shape of the object;
• the location where each image is taken


Vanishing line detection
Given an image, detect orthogonal line segments of the object


2. Landmark placement
INPUT: Landmark models in local coordinate systems

OUTPUT: Landmark models on a global coordinate systems

Landmark placement process
1. Obtain pairwise spatial relationship between adjacent landmarks
2. Place adjacent landmarks on the common ground

Spatial Relation Acquisition
Landmark placement
Click-Rotate-Click (CRC)
Click- Walk -Click (CWC)
Spatial relation acquisition
Click-Rotate-Click (CRC)
Click-Walk-Click (CWC)
Spatial relation acquisition-WalkCompass
Step recognition
Gyro-compass rotation recognition
Formulation
Multiple distance and orientation constraints
Results
3. Map augmentation
INPUT: Landmark models on a global coordinate systems

OUTPUT: Final floor plan

Wall Reconstruction
(a) two nearly collinear segments
Room Shape
Data-gathering micro-task
CWC inside one room

Step 1.
determine initial/final locations
Two camera locations as anchor points
Step 2
. use trajectories to build an occupancy grid map.
Step 3
. Thresholding and smoothing.
Hallway Structure
Augmentation process:
a. Get Landmark configuration
b. Form external boundary of hallway
c. Determine camera position
d. Determine trajectories
e. Build occupancy grid map
f. Binarization with a threshold
g. Smoothing Alpha-shape

(b) two nearly perpendicular segments
(c) two nearly
opposite segments
(a) Example scenario
(b) convex hull of all wall segments
(c) one possible output of the greedy method
(d) minimal weight matching using distance as weight
(e) our minimal weight matching method.

Wall Reconstruction
Evaluation
Procedure
3 stories of malls: 150x75m and 140x40m
8,13,14 store entrances as landmarks
150 photos for each landmark
182,184,151 CRC measurements
24 CWC measurements in story 3
96,106,73 user traces along hallway
~7 traces inside each store




CrowdInside++ discussion
Crowdinside:
several assumptions not always found in real world.
Crowdinside++ :
Artificial improvements


Miss a few small-sized stores
RMSE and maximum error: 4x of Jigsaw
Hallway shape: ~30% less than Jigsaw
Precision~64%, Recall~48%, F-score~55%

Results of CrowdInside++
Results

Wall segment detection accuracy: 91.7% 88.2% 100%
Wall segment detection precision: 100 % 100 % 100%
Not detected segments due to extreme angles.
100-150 images for each landmark are appropriate.
Store position error 1-2m
Store orientation error 5-9 degrees
Detailed results
Landmark modeling
Landmark Placement
Hallway shape
Precision~80%, Recall~90%, F-score~84%
25.6% 28.3% 28.9%
Small considering some parts of room not accessible.
Room size
Related work
Floor plan construction
CrowdInside
Reconstructs users’ mobile trajectories, but used many “anchor points” .
Jiang et. al
Detect similarities in WiFi signatures between different segments to find their adjacency.
Walkie-Markie:
Uses WiFi signals, but it uses locations where the trend of WiFi signal strength reverses
direction as anchor points.
MapGenie:
Uses mobile trajectories as well using foot-mounted IMU.

SLAM
Simultaneous Localization And Mapping :
Too many sensors, odometry, gyroscope, depth/stereo cameras and laser rangers.

3D construction in vision
Need continuous intervention
Plans are essentially 2D

Localization with vision techniques
Sextant:
Localization of user
Multiple sensors

Conclusion
Produces plans of complex indoor environments
Solves lack of floor plans at service providers.
Reconsruct floor plans from mobile users’ data,
Avoiding the intensive efforts and time needed in business negotiations or environment surveys.
Reasonably complete and accurate locations/orientations of landmarks, hallways and rooms.
Antonio Reyes Lúa
Luis A. González G.
Full transcript