Table 3 RMSEs, LOGLIKs, and KLDs of slip models learned in Evaluation 1. Bold numbers indicate best results among the three methods for each target domain
From: Terrain traversability prediction for off-road vehicles based on multi-source transfer learning
Target domain | Method | RMSE | LOGLIK | KLD |
|---|---|---|---|---|
Rough sand | GPR-naive | 0.749 | –4414.641 | 366.378 |
GPR-conventional | 0.151 | –304.836 | 39.989 | |
MS-TGPR (proposed) | 0.213 | 144.511 | 23.088 | |
Gravel with sand | GPR-naive | 0.489 | –2525.338 | 488.280 |
GPR-conventional | 0.142 | 102.149 | 11.091 | |
MS-TGPR (proposed) | 0.104 | 131.126 | 15.669 | |
Sand over bedrock | GPR-naive | 0.572 | –2586.805 | 900.773 |
GPR-conventional | 0.356 | –2178.509 | 350.095 | |
MS-TGPR (proposed) | 0.314 | –87.785 | 15.430 | |
Sand-covered bedrock | GPR-naive | 0.460 | –2002.584 | 861.105 |
GPR-conventional | 0.388 | –1005.425 | 421.268 | |
MS-TGPR (proposed) | 0.227 | –111.624 | 7.758 |