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2 changes: 1 addition & 1 deletion gtsam/nonlinear/doc/PriorFactor.ipynb
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"\n",
"## Usage Considerations\n",
"\n",
"- **Noise Model**: The choice of noise model is critical as it determines how strongly the prior is enforced. A tighter noise model implies a stronger belief in the prior. *Note that very strong priors might make the condition number of the linear systems to be solved very high. In this case consider adding a [NonlinearEqualityFactor]\n",
"- **Noise Model**: The choice of noise model is critical as it determines how strongly the prior is enforced. A tighter noise model implies a stronger belief in the prior. *Note that very strong priors might make the condition number of the linear systems to be solved very high. In this case consider adding a [NonlinearEqualityFactor](./NonlinearEquality.ipynb)\n",
"- **Integration with Other Factors**: The `PriorFactor` is typically used in conjunction with other factors that model the system dynamics and measurements. It helps anchor the solution, especially in scenarios with limited or noisy measurements.\n",
"- **Applications**: Common applications include SLAM (Simultaneous Localization and Mapping), where priors on initial poses or landmarks can significantly improve map accuracy and convergence speed."
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