Visual Simultaneous Localization and Mapping Algorithm Combining Mixed Attention Instance Segmentation

Fig. 8. Matching results in 00 sequence. (a) SURF feature matching results; (b) ORB feature matching results; (c) proposed algorithm feature matching results

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Fig. 9. Operating trajectories in different sequences on KITTI. (a) 10 sequence; (b) 01 sequence; (c) 06 sequence; (d) 07 sequence; (e) 09 sequence; (f) 00 sequence

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Fig. 10. Processing time per frame on three algorithms. (a) ORB-SLAM2; (b) DS-SLAM; (c) proposed algorithm

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Fig. 11. TurtleBot3 Burger

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Fig. 12. Real experimental environment scene. (a) Real scene; (b) layout plan

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Fig. 13. Image of pentacle position for the first time. (a) Instance segmentation result of pre-improved algorithm; (b) instance segmentation result of proposed algorithm

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Fig. 14. Image of pentacle position for the second time. (a) Instance segmentation result of pre-improved algorithm; (b) instance segmentation result of proposed algorithm

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Fig. 15. Operating trajectory in real scene

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Table 1. Main parameter of mixed attention backbone network

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Table 1. Main parameter of mixed attention backbone network

Number	Type	Area	Value
0	Conv1_x-Output	ResNet-50，Conv1_x	（64，H/4，W/4）
1	Conv2_x-Output	ResNet-50，Conv2_x	（256，H/4，W/4）
2	Conv3_x-Output	ResNet-50，Conv3_x	（512，H/8，W/8）
3	Conv4_x-Output	ResNet-50，Conv4_x	（1024，H/16，W/16）
4	Conv5_x-Output	ResNet-50，Conv5_x	（2048，H/32，W/32）
5	Upsample stride	FPN	2
6	Convolution kernel size	Spatial attention	7×7
7	Activation function	Spatial attention	Sigmoid
8	Activation function	Channel attetion-MLP	ReLU
9	Activation function	Channel attention	Sigmoid

Table 2. Comparison of algorithm test results in AP
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Table 2. Comparison of algorithm test results in AP
Algorithm Backbone AP /%
AP AP₅₀ AP₇₅ AP_S AP_M AP_L
Mask-RCNN ResNet-50-FPN 33.4 54.9 35.3 14.7 35.2 50.1
Proposed algorithm ResNet-50-MAM-FPN 34.9 57.5 36.9 15.3 36.9 52.5

Table 3. Comparison of effective matching rate and matching time on KITTI

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Table 3. Comparison of effective matching rate and matching time on KITTI

Sequence	SURF				ORB				Proposed algorithm
Sequence	Matching pairs	Effective matching pairs	Effective matching rate /%	Matching time /s	Matching pairs	Effective matching pairs	Effective matching rate /%	Matching time /s	Matching pairs	Effective matching pairs	Effective matching rate /%	Matching time /s
00	1235	1002	81.1	0.1156	512	395	77.1	0.0089	494	392	79.4	0.0115
01	1254	1030	82.1	0.1172	490	381	77.8	0.0084	489	396	81.0	0.0097
06	1560	1264	81.0	0.1405	607	457	75.3	0.0094	524	424	80.9	0.0122
07	1438	1196	83.2	0.1281	530	405	76.4	0.0090	507	426	84.0	0.0119
09	1320	1088	82.4	0.1261	507	390	76.9	0.0086	501	412	82.2	0.0102
10	1480	1210	81.8	0.1364	552	424	76.8	0.0092	514	424	82.5	0.0121
Average	1381	1132	81.9	0.1273	533	409	76.7	0.0089	505	412	81.7	0.0113
Variance	14376	9476	0.57	0.083×10^-3	4568	648	0.58	0.115×10^-6	140	190	2.1	0.937×10^-6

Table 4. Comparison of operating results on KITTI

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Table 4. Comparison of operating results on KITTI

Sequence	ORB-SLAM2			DS-SLAM			Proposed algorithm
Sequence	Average distance Error /m	Average angle Error /m	Precision rate of loop detection /%	Average distance Error /m	Average angle Error /m	Precision rate of loop detection /%	Average distance Error /m	Average angle Error /m	Precision rate of loop detection /%
10	3.15	1.55		2.62	0.94		2.01	0.82
01	3.26	1.39		3.01	0.88		2.32	0.79
06	2.99	1.57	77.9	2.51	0.79	82.3	2.38	0.73	86.4
07	3.05	1.30		2.72	0.61		2.53	0.50
09	3.11	1.43		2.87	0.85		2.14	0.72
00	3.64	1.24	76.6	2.94	0.97	80.4	2.54	0.87	84.7

Table 5. Operation parameters setting
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Table 5. Operation parameters setting
Variable Parameter Value
Running velocity Vs 0.15 m/s
Rotating velocity V_θ 2.1 rad/s
Range of directional Θ ［0，2π］
Camera sampling
frequency
H 30 frame/s

Table 6. Comparison of running results in real scene
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Table 6. Comparison of running results in real scene
Index RMSE /m Time /s Precision rate of loop detection /%
Proposed algorithm 0.52 270 85.3

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Haowei Jiang, Mengyuan Chen, Xuechao Yuan. Visual Simultaneous Localization and Mapping Algorithm Combining Mixed Attention Instance Segmentation[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1028008

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