Neither is GPS accurate enough – especially indoors.Let’s start with the basics, as described by Cheeseman et al. 458.6 510.9 249.6 275.8 484.7 249.6 772.1 510.9 458.6 510.9 484.7 354.1 359.4 354.1 9 0 obj Chicken-or-egg problem map ... Algorithm DONE Apply & DONE . If it’s certain enough that the keypoints match, it uses the additional data to refine the camera pose.New map points are created by triangulating matching keypoints from connected frames. 33 0 obj >> /BaseFont/YZFJNJ+CMR17 658.3 329.2 550 329.2 548.6 329.2 329.2 548.6 493.8 493.8 548.6 493.8 329.2 493.8 The principle of SLAM is: The best explanation of your sensor data is the simplest one. Not all SLAM algorithms fit any kind of observation (sensor data) and produce any map type. The embedded processor runs the entire SLAM algorithm onboard, analyzes the stereo images and fuses all sensor information together into 6DoF tracking, all while using less than 1.5W of power. If the similarity exceeds a threshold, the algorithm knows that the user returned to a known place; but inaccuracies on the way might have introduced an offset.By propagating the coordinate correction across the whole graph from the current location to the previous place, the map is updated with the new knowledge.Quick reality check: what’s used in today’s mobile AR? /Length 4766 and Fast-SLAM solve the SLAM problem by using probabilities to control for errors in the robot’s sensors. /FirstChar 33 548.6 548.6 548.6 548.6 548.6 548.6 548.6 548.6 548.6 548.6 548.6 329.2 329.2 329.2 874 706.4 1027.8 843.3 877 767.9 877 829.4 631 815.5 843.3 843.3 1150.8 843.3 843.3 272 272 489.6 544 435.2 544 435.2 299.2 489.6 544 272 299.2 516.8 272 816 544 489.6 /Type/Font /Widths[342.6 581 937.5 562.5 937.5 875 312.5 437.5 437.5 562.5 875 312.5 375 312.5

FRANKL: “dk6033_c009” — 2005/12/23 — 18:18 — page 339 — #9 Map Building and SLAM Algorithms 339 Simultaneous Localisation and Mapping (SLAM): Part I The Essential Algorithms Hugh Durrant-Whyte, Fellow, IEEE, and Tim Bailey Abstract|This tutorial provides an introduction to Simul-taneous Localisation and Mapping (SLAM) and the exten-sive research on SLAM that has been undertaken over the past decade. /BaseFont/BMTLVS+CMBX8 500 500 500 500 500 500 500 500 500 500 500 277.8 277.8 277.8 777.8 472.2 472.2 777.8 /LastChar 196 299.2 489.6 489.6 489.6 489.6 489.6 734 435.2 489.6 707.2 761.6 489.6 883.8 992.6 692.5 323.4 569.4 323.4 569.4 323.4 323.4 569.4 631 507.9 631 507.9 354.2 569.4 631 >> 593.8 500 562.5 1125 562.5 562.5 562.5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
/BaseFont/CLFQRQ+CMR7 This paper provides a discussion of these two algorithms and compares their run times and the accuracy of the maps they produce.

413.2 590.3 560.8 767.4 560.8 560.8 472.2 531.3 1062.5 531.3 531.3 531.3 0 0 0 0 30 EKF SLAM – Correction ! << 24 0 obj /LastChar 196 This idea is not new; in fact, it is at the core of recent algorithms by Newman [37] and Csorba [7, 8], and it is related to an algorithm … << SLAM is the process by which a mobile robot /FirstChar 33 /Widths[329.2 550 877.8 816 877.8 822.9 329.2 438.9 438.9 548.6 822.9 329.2 384 329.2 548.6 329.2 329.2 493.8 274.3 877.8 603.5 548.6 548.6 493.8 452.6 438.9 356.6 576
Abstract—SLAM (Simultaneous Localization and mapping) is one of the most challenging problems for mobile platforms and there is a huge amount of modern SLAM algorithms. in In a perfect world, you’d have perfect information about the exact location of everything. 458.6] 249.6 719.8 432.5 432.5 719.8 693.3 654.3 667.6 706.6 628.2 602.1 726.3 693.3 327.6 761.6 679.6 652.8 734 707.2 761.6 707.2 761.6 0 0 707.2 571.2 544 544 816 816 272 /Widths[272 489.6 816 489.6 816 761.6 272 380.8 380.8 489.6 761.6 272 326.4 272 489.6 27 0 obj 826.4 295.1 531.3] 324.7 531.3 590.3 295.1 324.7 560.8 295.1 885.4 590.3 531.3 590.3 560.8 414.1 419.1 544 516.8 380.8 386.2 380.8 544 516.8 707.2 516.8 516.8 435.2 489.6 979.2 489.6 489.6 /Subtype/Type1 /LastChar 196 Initially, the match is calculated between two frames – but it can later be extended to additional frames.Another key step in a SLAM algorithm is loop detection and loop closing: ORB-SLAM checks if keypoints in a frame match with previously detected keypoints from a different location.

/LastChar 196 687.5 312.5 581 312.5 562.5 312.5 312.5 546.9 625 500 625 513.3 343.8 562.5 625 312.5 /Type/Font

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 734.7 1020.8 952.8 /Name/F7 /BaseFont/KPIDBY+CMBX12 /LastChar 196 This paper describes a SLAM algorithm that represents map posterior by relative informa-tion between features in the map, and between the map and the robot’s pose. The triangulation is based on the 2D position of the keypoint in the frames, as well as the translation and rotation between the frames as a whole. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 672.6 877.8 822.9 741.7 713.2 796.5 12 0 obj Especially, Simultaneous Localization and Mapping (SLAM) using cameras is referred to as visual SLAM (vSLAM) because it is based on visual information only. 458.6 458.6 458.6 458.6 693.3 406.4 458.6 667.6 719.8 458.6 837.2 941.7 719.8 249.6 708.3 795.8 767.4 826.4 767.4 826.4 0 0 767.4 619.8 590.3 590.3 885.4 885.4 295.1 >> Simultaneous Localization and Mapping (SLAM) ! 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 663.6 885.4 826.4 736.8 /Widths[277.8 500 833.3 500 833.3 777.8 277.8 388.9 388.9 500 777.8 277.8 333.3 277.8 It calculates this through the spatial relationship between itself and multiple keypoints. /Type/Font 500 500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 625 833.3 /Subtype/Type1 761.6 679.6 652.8 734 707.2 761.6 707.2 761.6 0 0 707.2 571.2 544 544 816 816 272 462.4 761.6 734 693.4 707.2 747.8 666.2 639 768.3 734 353.2 503 761.2 611.8 897.2 An intuitive way to address the SLAM problem is via its so-called graph-based formulation. /Subtype/Type1 If you have the freedom of placing However, mobile Augmented Reality usually doesn’t have the luxury of known beacons.


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